Toward an interdisciplinary perspective

TOWARDS AN

INTERDISCIPLINARY

PERSPECTIVE ON THE LIFE

COURSE

i

ADVANCES IN LIFE COURSE

RESEARCH

Series Editor: Timothy Owens

Recent Volumes:

Volume 1: Work, Retirement and Social Policy, 1986

Edited by Zena Smith Blau

Volume 2: Family Relations in Life Course Perspective, 1986

Edited by David I. Kertzer

Volume 3: Personal History Through the Life Course, 1993

Edited by R. S. Olusegun

Volume 4: Delinquency and Disrepute in the Life Course,

1995

Edited by Zena Smith Blau and John Hagen

Volume 5: Self and Identity Through the Life Course in

Cross-Cultural Perspective, 2000

Edited by Timothy J. Owens

Volume 6: Children at the Millennium: Where Have

We Come From, Where Are We Going?, 2001

Edited by Sandra L. Hofferth and

Timothy J. Owens

Volume 7: New Frontiers in Socialization, 2002

Edited by Richard A. Settersen, Jr. and

Timothy J. Owens

Volume 8: Changing Life Patterns in Western Industrial

Societies, 2004

Edited by Janet Zollinger Giele and Elke Holst

Volume 9: The Structure of the Life Course: Standardized?

Individualized? Differentiated?, 2005

Edited by Ross Macmillanii

ADVANCES IN LIFE COURSE RESEARCH VOLUME 10

TOWARDS ANINTERDISCIPLINARYPERSPECTIVE ON THE

LIFE COURSE

EDITED BY

RENE LEVYUniversite de Lausanne

PAOLO GHISLETTAUniversite de Geneve

JEAN-MARIE LE GOFFUniversite de Lausanne

DARIO SPINIUniversite de Lausanne

ERIC WIDMERUniversite de Lausanne

Amsterdam – Boston – Heidelberg – London – New York – Oxford

Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo

iii

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CONTENTS

PREFACE ix

LIST OF CONTRIBUTORS xi

INTRODUCTION

WHY LOOK AT LIFE COURSES IN ANINTERDISCIPLINARY PERSPECTIVE?

Rene Levy and the Pavie Team 3

PART I: AGENCY AND STRUCTURE

STRUCTURE, AGENCY, AND THE SPACE BETWEEN:ON THE CHALLENGES AND CONTRADICTIONS OFA BLENDED VIEW OF THE LIFE COURSE

Richard A. Settersten, Jr. and Lynn Gannon 35

AGENCY, EVENTS, AND STRUCTURE AT THE ENDOF THE LIFE COURSE

Victor W. Marshall 57

LOOKING AT AMBIVALENCES: THECONTRIBUTION OF A ‘‘NEW-OLD’’ VIEW OFINTERGENERATIONAL RELATIONS TO THE STUDYOF THE LIFE COURSE

Kurt Luscher 93

v

PART II: TRANSITIONS

AGENCY AND STRUCTURE IN EDUCATIONALATTAINMENT AND THE TRANSITION TOADULTHOOD

Jeylan T. Mortimer, Jeremy Staff and Jennifer C. Lee 131

NON-NORMATIVE LIFE COURSE TRANSITIONS:REFLECTIONS ON THE SIGNIFICANCE OFDEMOGRAPHIC EVENTS ON LIVES

Frank F. Furstenberg 155

THE SECRET OF TRANSITIONS: THE INTERPLAY OFCOMPLEXITY AND REDUCTION IN LIFE COURSEANALYSIS

Katherine Bird and Helga Kruger 173

PART III: BIOGRAPHICAL RECONSTRUCTION

LIFE COURSE TRANSITIONS AND SOCIALIDENTITY CHANGE

Nicholas Emler 197

THE IMPACT OF PERSONALITY AND LIVINGCONTEXT ON REMEMBERING BIOGRAPHICALTRANSITIONS

Pasqualina Perrig-Chiello and Walter J. Perrig 217

STUDYING LIVES IN TIME: A NARRATIVEAPPROACH

Dan P. McAdams 237

CONTENTSvi

PART IV: METHODOLOGICAL INNOVATIONS

LIFE COURSE ANALYSIS: TWO (COMPLEMENTARY)CULTURES? SOME REFLECTIONS WITH EXAMPLESFROM THE ANALYSIS OF THE TRANSITION TOADULTHOOD

Francesco C. Billari 261

LIFE COURSE DATA IN DEMOGRAPHY ANDSOCIAL SCIENCES: STATISTICAL AND DATA-MINING APPROACHES

Gilbert Ritschard and Michel Oris 283

FIVE STEPS IN LATENT CURVE MODELING WITHLONGITUDINAL LIFE-SPAN DATA

John J. McArdle 315

AFTERTHOUGHTS

INCITATIONS FOR INTERDISCIPLINARITY IN LIFECOURSE RESEARCH

Rene Levy, Paolo Ghisletta, Jean-Marie Le Goff, DarioSpini and Eric Widmer

361

Contents vii

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viii

PREFACE

This volume has grown out of an international colloquium on interdisciplinarity

in life course research organized by the Center for life course and life style studies

(PAVIE) at the Universities of Lausanne and Geneva (Switzerland) in October

2003. The contributors were asked to react to a working document prepared by

the organizers but had entire freedom as to how they wanted to do it.

The authors represent four disciplines directly involved in life course research:

developmental and personality psychology, social psychology, sociology, and

social demography. The focus on these four social-science disciplines in a ven-

ture into interdisciplinarity should certainly not be read as a programmatic

limitation of our wished-for disciplinary scope of collaboration. On the con-

trary, we fully acknowledge that other disciplines have made important con-

tributions to life course research and that they are by no means located at its

margins – let us only think of social history or political science. The potential

role of biology and social medicine may be more controversial, but there again,

our selection is pragmatic and provisional, not exclusive and even less ideolog-

ical. The aim of this volume is to contribute to the enlargement and strength-

ening of collaboration between all disciplines able to develop an interest in life

course or life span research.

The colloquium as well as some of the subsequent work necessary for this

volume to materialize has been financed by the Swiss National Science Foun-

dation, and a series of services and facilities have been contributed by the Uni-

versity and the Municipality of Lausanne, an indispensable help for which we

are very grateful. Beyond the contributors who are, of course, the main actors of

this volume, we owe special thanks to Tatiana Lazzaro who did a great job all

along this enterprise, beginning with the organization of the colloquium and

ending with a large amount of administrative and editing services she delivered

with exceptional enthusiasm, competence and efficacy. We also thank Tim

Owens, the editor of this series, who was highly interested in our endeavor from

the outset and extremely helpful for its taking the shape our readers can ap-

preciate now.

Rene Levy, Paolo Ghisletta, Jean-Marie Le Goff, Dario Spini and

Eric Widmer (Editors)

Lausanne/Geneva

ix


x

LIST OF CONTRIBUTORS

Francesco C. Billari Istituto di Metodi Quantitativi, Universita

Bocconi and Innocenzo Gasparini Institute for

Economic Research, Milano, Italy

Katherine Bird Universitat Bremen, EMPAS, Bremen,

Germany

Nicholas Emler Department of Psychology, University of

Surrey, Guildford, UK

Frank F. Furstenberg University of Pennsylvania, Department of

Sociology, Philadelphia, PA, USA

Lynn Gannon Department of Sociology, Case Western

Reserve University, Cleveland, OH, USA

Paolo Ghisletta Centre Interfacultaire de Gerontologie (CIG) et

Faculte de Psychologie et des Sciences de

I’education, Universite de Geneve, Centre

lemanique d’etude des parcours et modes de vie,

Universites de Lausanne et de Geneve, Geneve,

Switzerland

Helga Kruger Universitat Bremen, EMPAS, Bremen,

Germany

Jennifer C. Lee Life Course Center, Department of Sociology,

University of Minnesota, Minneapolis, MN,

USA

Jean-Marie Le Goff Centre lemanique d’etude des parcours et

modes de vie, Universite de Lausanne,

Lausanne, Switzerland

Rene Levy Centre lemanique d’etude des parcours et



xi

Kurt Luscher Fachgruppe Soziologie, Universitat Konstanz,

Germany

Victor W. Marshall UNC Institute on Aging, University of North

Carolina at Chapel Hill, NC, USA

Dan P. McAdams Northwestern University, Evanston, IL,

USA

John J. McArdle Department of Psychology, University of

Virginia, Charlottesville, VA, USA

Jeylan T. Mortimer Life Course Center, Department of Sociology,

University of Minnesota, Minneapolis, MN,

USA

Michel Oris Department of Economic History, University

of Geneva, Geneva, Switzerland

Walter J. Perrig Institut fur Psychologie, Universitat Bern, Bern,

Switzerland

Pasqualina Perrig-

Chiello

Institut fur Psychologie, Universitat Bern, Bern,

Switzerland

Gilbert Ritschard Department of Econometrics, University of

Geneva, Geneva, Switzerland

Richard A. Settersten,

Jr.

Department of Sociology, Case Western

Reserve University, Cleveland, OH, USA

Dario Spini Centre lemanique d’etude des parcours et



Jeremy Staff Department of Sociology and Crime, Law and

Justice, The Pennsylvania State University,

University Park, PA, USA

Eric Widmer Centre lemanique d’etude des parcours et



LIST OF CONTRIBUTORSxii

INTRODUCTION

1


2

WHY LOOK AT LIFE COURSES IN

AN INTERDISCIPLINARY

PERSPECTIVE?

Rene Levy and the Pavie Team1

CHALLENGES OF LIFE COURSE RESEARCH

After decades of a rather marginal existence and little coherence in its de-

velopment, life course research is definitely coming of age. There recent

signs of consolidation and first attempts at reaping the scattered harvest of

research in various disciplines, especially in the form of a first handbook of

life course research (Mortimer & Shanahan, 2003), of first attempts at

interdisciplinary dialogue around specific approaches such as Baltes’ life-

span psychology2 (Staudinger & Lindenberger, 2003) and of a specialized

annual review (in which this volume is published). Nevertheless, life course

scholars still seem to be a small handful ‘‘digging’’ on the fringe of their

disciplinary mainstreams, as yet with little influence on more established

fields of research. Why are we, i.e., life course researchers, so keen on life

courses? What is there so special about life course research? We feel in fact

that there are a number of specific challenges life course researchers have to

confront and answer.

A first, global and not very differentiated reason for finding it particularly

interesting could be that it encompasses all we find important about human

life and that everything humanly relevant is in the life course.

Towards an Interdisciplinary Perspective on the Life Course

Advances in Life Course Research, Volume 10, 3–32

Copyright r 2005 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10014-8

3

A second answer, of the same global kind but a bit more specific, could be

that only a life course approach takes fully into account the fact that our

lives are ongoing processes and not just single states or events that can be

adequately captured and understood using snapshots.

A third reason of attractiveness, even more fascinating but also demand-

ing, is the cross-cutting and integrative nature of the life course perspective

with respect to most of our more conventional, institutionalized disciplinary

specialties. To illustrate this statement, take the case of sociology.3 There are

special sociologies of childhood, of youth or adolescence, (maybe soon also

of post-adolescence,) of retirement, and of aging. Each ‘‘age group’’ is

treated in a more or less static perspective and therefore reified as a some-

how homogenous social category and not as a phase in the process of life

course unfolding. There are special sociologies of the family, of work, of

labor markets, of leisure, of stratification and mobility, of voluntary asso-

ciations, of social movements and so on. Each social field is mainly treated

as an isolated entity with its inner logic and specific pathways for the in-

dividuals who participate in these fields. Life-phase specific and sector spe-

cific sociologies have developed their specialized perspectives, slicing up the

lives and contexts of the ‘‘whole individuals’’ we pretend to be into various

aspects, types of social relations, phases, or fields of action. Likewise, in

psychology, ontogeny has been regarded by many as occurring almost

differently in distinct fragments of the life span. Much as in sociology, the

American Psychological Association, among its 53 divisions, has Division

7 for Developmental Psychology and Division 20 for Adult Development

and Aging. Similarly, Division 12 is for the Society of Clinical Psychology,

while Division 53 for the Society of Clinical Child and Adolescent Psy-

chology. Again, the fragmentation of the life course has been, for some

aspects at least, institutionalized. Against this reductionistic, fragmenting

tendency, Settersten (2003b, p. 196) has rightly made a strong point of ‘‘The

importance of understanding people in whole (over time) and as wholes

(studying larger profiles of traits and characteristics rather than single var-

iables)’’. Without an encompassing perspective, life course research cannot

meet the challenge of its very raison d’etre.

On a more fundamental level, there is a fourth challenge which is already

included in these remarks but not explicitly spelled out: life course analysis is

one of the rare ways social sciences have developed so far to conceptualize

time, not just as a physical happening whose whereabouts escape our the-

oretical understanding, but as something that is culturally, socially and also

individually molded, reworked and ‘‘constructed’’, something that has both

a subjective and an objective existence, and whose very objectivity is the

RENE LEVY AND THE PAVIE TEAM4

result of social objectiveness rather than of mere ‘‘being there’’ like the

eternal rhythm of atomic oscillation. Settersten (1999) has entitled his recent

stock-taking book ‘‘Lives in Time and Place’’, referring to two basic di-

mensions of the real world that both are not just physical ‘‘givens’’, but

socially constructed: social space and social time. In classical developmental

psychology, most research was concerned with charting along the age axis

the newly acquired skills and mastered developmental tasks by children.

Refined descriptions of what the child could achieve at what age flourished,

and in various domains age-related norms were established. Wohlwill (1970)

was one of the first to question the limitations of the mere descriptive role of

chronological age. (We shall take up the question of what status to give to

age in our theorizing about life courses in the final chapter.)

In short, in both a humanistic and in a scientific view, life course research

is fascinating because it forces us to break down traditional limitations of

understanding.

THE NECESSITY OF INTERDISCIPLINARITY

If adopting a life course perspective in a single study does not exclude fo-

cusing on a specific aspect of peoples’ lives, the perspective as such is nec-

essarily integrative: it has to bridge a number of institutionalized chasms in

and between social science disciplines. These chasms are sometimes openly

discussed, even questioned, and sometimes rather taken for granted. At any

rate they tend to limit our awareness, inducing what we may call an interna-

listic perspective, artificially bounded by the boundaries of the specialty in

which our scholarly activity is organized. We have already mentioned the two

dimensions life course researchers have to bridge in their work, the age-group

axis and the life-spheres axis. At least three others are of equal importance:

� What sociologists, and others as well, are used to call the macro–micro

link, i.e., more generally, the fact that one basic dimension in the com-

plexity of reality is the nesting of encompassing systems of various levels;4

this dimension (we might call it systemic differentiation to distinguish it

from two others, hierarchization or parallelism) takes all its importance in

the debate about the institutionalization of life course patterns as opposed

to actorial agency although it is more often invoked as important than

really taken into account.� Gender differences or more generally the social relationships between the

sexes,5 but also a number of other cases of strong social differentiation of

both a cultural and a structural kind that have strong incidences on the

Why Look at Life Courses in an Interdisciplinary Perspective? 5

level of individual identities (like ethnic or religious group membership,

etc.), that are often leveled out by static analyses or even ignored by gender-

blind or more generally ‘‘differentiation-blind’’ theoretical models.6

� Linked lives, the strong interdependencies between the life courses of re-

lated persons that remind us that the real object of most of the social

sciences are relations between actors and not just monadic individuals as

some current mainstream paradigms would have it.7

If we are to bridge successfully all these gaps, with their thematical,

conceptual and methodological implications, one may object that we have

to become scientific geniuses. As social scientists we know that individual

mastery is not all, that groups are frequently wiser than the ‘‘addition’’ of

their individual members, so teamwork is certainly a way out of this prob-

lem. Working integratively in the sense just mentioned calls for teamwork.

Among the gaps that work on life courses intrinsically needs to bridge

figures also the one between scientific disciplines, above all in the social

sciences (including, of course, psychology). The arguments for inter-

disciplinarity in this area may not be fundamentally different from those

applying to other themes – as soon as we get interested in a real world

problem and not only an epistemological slice of it, we cannot be satisfied by

adopting one disciplinary perspective only. Most often, interdisciplinarity is

claimed for problem-oriented or ‘‘applied’’ research. If the research objec-

tive is to resolve a problem, disciplinary purity and coherence is of no

interest, we mobilize all promising resources whatever their disciplinary

origin and theoretical ramifications. This may not be the primary orienta-

tion of life course research. But there is another important and sufficient

motive for interdisciplinarity in this area that we have already encountered:

the holist perspective on individuals and their development in social context

which cannot be contented by any single aspect alone, be it sectoral or

disciplinary. It is no accident that interdisciplinarity is frequently called for

in this area. When formulating his ‘‘life course paradigm’’, Elder (1995)

included interdisciplinarity explicitly, but the overriding research praxis

remains monodisciplinary.8

A further question is then: what should we mean by interdisciplinarity?

We usually use the term in a very general sense, meaning just going beyond

the limits of any one of our habitual disciplines (monodisciplinarity). In

order to be more precise, we may join an emergent distinction between

� multidisciplinarity (or pluridisciplinarity), meaning the cooperation of

several disciplines that work on complementary aspects of a common

overall question,


� interdisciplinarity, meaning the integrated cooperation of disciplines on

the basis of common concepts, and� transdisciplinarity, meaning the scientific work on the basis of common

concepts that do not belong to any specific discipline and that may also

include the points of view of extra-scientific actors concerned by the

question under study.9

There is no general, absolute value hierarchy to be established between

these possibilities, ‘‘trans’’ is not necessarily better than ‘‘multi’’ and there

may very well be problems whose ‘‘mono’’ study is quite adequate. The

proper level between mono- and transdisciplinarity must be identified for

each research enterprise, and the optimum may very well be an evolving mix

and not a one-level solution fixed once and forever. But what is certainly

necessary is the capacity of people and teams to work on these different

levels. In order for such an enterprise to work, a more or less clearly stated

common goal is of course essential, but other conditions are of equal im-

portance: the real desire of the participants to work together, their ac-

knowledgment of the potential of mutual learning rather than competition,

even less the pursuit of disciplinary hegemony, and a fundamental working

knowledge allowing for an understanding and appreciation of the concep-

tual and methodological panoply of neighboring disciplines.

There is a huge gap between the almost unisonous choir of public voices

asking for interdisciplinarity, and scientific everyday practice that is needed

to discover the real difficulties of such an endeavor to begin with. A realistic

approach must probably start from the principle with a view to interdisci-

plinary convergence or at least cooperation, differences should not be si-

lenced, but discussed and integrated. Well-placed protagonists assure us that

this is no easy business – witness, as one example, Mayer (2002) who coolly

states on the encounter of life course sociology and life-span psychology (in

which he has been engaged himself): ‘‘In retrospect, despite all the strong

mutual recognition and reinforcement, surprisingly little convergence and

integration has actually occurred’’. There is definitely still some way to make;

the present volume is meant to bring us a step ahead on this way.

DIFFICULTIES OF INTERDISCIPLINARITY

As long as not many social scientists themselves have a really interdisciplinary

education and working profile, interdisciplinary work is bound to be team-

work. Along with more current organizational questions, interdisciplinary


teams have to confront quite real problems in their everyday functioning,

even if these may often seem somewhat trivial when looked at from a the-

oretical vantage point. These practical difficulties need to be taken seriously

in order to overcome them. Let us briefly look into some of them. But an

assured rootedness in their own disciplines of scholars working together in an

interdisciplinary team is most probably a precondition for successful inter-

disciplinarity, even though this may appear as a paradox.

A first kind of difficulty to be clarified and surpassed on the way to

interdisciplinary teamwork is in the area of vocabulary. Our disciplines use

some identical terms with quite different meanings (e.g., ‘‘norm’’ has an

entirely cultural, obligational ring in sociology, but the objectivist meaning

of statistical prevalence – or even biological ‘‘normality’’ – in developmental

psychology; or the different semantic extension of the notion of ‘‘micro’’

and ‘‘macro’’ between the same two disciplines). The other way round, we

sometimes use different terms for the same things (e.g., in the methodolog-

ical area). One example seems to pose few problems: the more sociological

or demographical term of life course seems to overlap very largely with the

more psychological one of life span. We therefore consider them in this

chapter as synonyms. Another, probably minor, example is different names

used in different disciplines for the same methods (e.g., survival analysis,

event history analysis, or, in French, ‘‘demographical analysis of bio-

graphies’’10 in the current terminology of demography all designate the same

or at least highly similar analytical methods; the same is true for multilevel

models – random effect models – mixed effect models and also hierarchical

models).

Other differences, that cannot be ruled out by mere lexical agreements,

are related to the typical frames of attention of the disciplines. Mainstream

(or traditional) developmental psychology focuses largely on very early (and,

to a lesser extent, very old) years of life while the bulk of sociological work

concerns the adult years (mostly without spelling out explicitly this horizon).

At first sight, social psychology does not seem to be very specific in this

respect; in fact, it shows only rarely an outspoken awareness of and interest

in processes of growing older or in the life course. But there is a hidden and

rarely acknowledged specificity inherent in much research in this discipline:

a large fraction of empirical research in social psychology, at least about

attitudes and representations, is done with the most accessible category of

experimental subjects, i.e., with students, who represent quite a specific age

group as well as a set of rather specific locations in social stratification.

Little is known about the impact of this double background specificity on

the results and scientific knowledge in this discipline. Two transitions have


attracted the attention of social demographers, the first from adolescence to

adulthood and the second the end of life (mortality).

There are, of course, many conceptual differences that may constitute as

many difficulties for mutual understanding and cooperation. Let us only

mention one example: the weight that different social science disciplines

usually attribute to biology vs. environment (or nature vs. nurture in a more

traditional formulation). While psychology has largely put aside earlier ap-

prehensions with respect to close contact with biology, sociology and even

more political sciences or social and cultural anthropology have up to now

maintained a greater distance; demography is hard to locate on this di-

mension, but would certainly have little hesitations to be situated near psy-

chology, and potentially even could stretch its interests as far as to reach not

only psychology or sociology, but also biology in the sense that it is cur-

rently interested in biologically structured events like birth and death. This

basic opposition has a close relative in the difference between internal and

external explanations of the ‘‘socialization vs. institutional channeling’’ va-

riety (e.g., concerning the probable impact of age-normative conceptions vs.

context or environmental conditions). Confronted to such differences in

basic assumptions that characterize the often implicit scientific cultures of

different disciplines, interdisciplinary collaboration needs above all the re-

nouncement of rigorous a priori stands and a pragmatic curiosity for the

other disciplines’ way of looking at things. Another important and poten-

tially dividing difference concerns the distinction between factual and rep-

resentational aspects that may be highlighted by the far-reaching attachment

to statistical description by demography on the one hand and the heavy

emphasis of most of social psychology on individual and collective repre-

sentations, on the other. If we push this line of thinking further, there are of

course situations where it may become very difficult to find common ground

between disciplines, like psychology and demography, because they are

normally interested in many different topics – but then, we are also leaving

the array where interdisciplinarity really makes sense.

PATHWAYS TO INTERDISCIPLINARITY

Beyond such unavoidable practical questions and their solutions, whose

importance must not be underestimated, we should also think about more

theoretically grounded pathways to real interdisciplinarity. In this section,

we wish to propose three such routes for practical exploration: direct the-

oretical linkages between concepts of different disciplines, identification of


formal characteristics of life courses that may acquire similar meanings for

different disciplines and specific substantial themes that may link different

disciplinary outlooks due to their transversal relevance. Our presentation will

necessarily remain abstract, with only limited illustrations, as our aim cannot

go beyond sketching some promising directions; their exploration is yet to

come, but hopefully, this outline can provoke some fruitful discussions that

help us progress in this sense. We feel moreover that systematic scrutiny of

the extant literature would produce a wealth of examples that have already

arisen out of research practice. Our proposal is probably little more than a

possible systematization of thoughts already present in our field.

THEORETICAL LINKAGES

A first possible pathway to the construction of interdisciplinarity in life

course research consists of creating theoretical linkages between concepts of

different disciplines. If this is not an explicit part of our everyday practice

when doing research, it is often implicitly present. If we take the example of

the relationship between sociology and social psychology, we may say that

many – perhaps all – sociological hypotheses located on the micro-

interactional level and concerning behavior of individuals and groups are

based on implicit assumptions of social–psychological mechanisms relating

the (sociological) concepts used. The most trivial such mechanism is some

kind of social rationality, be it along the lines of rational choice theory or

according to more complex paradigms. Less trivial examples are social

comparison, social learning, the pursuit of social recognition, reciprocity

and advantage, or conformity to social norms. Another, quite different

mechanism of that kind would be the one operating the transformation of

internal into external attribution in the course of social interactions leading

the implied actors to realize that each of them is not alone to experience a

given problem but that there is a social category of people with whom they

share the problem in question (classical example: the formation of the labor

movement, and social movements more generally), and the personally ‘‘al-

leviating’’ consequence of such processes of collectivization of subjectively

felt problems. Many hypotheses about social behavior, e.g., concerning bio-

graphical decisions with a view to social mobility, currently assume the

functioning of such mechanisms without normally making them explicit,

even less testing them. Similar relationships may exist or be developed

between other disciplines.


A slightly different example is the formulation of direct interdisciplinary

hypotheses, i.e., hypotheses linking dimensions ‘‘belonging’’ to different

disciplines. Stating more explicitly Baltes’ (1987, 1997) thesis of contextu-

ality in psychological development directly leads to such hypotheses, for

instance in postulating how structural locations of individuals (in the larger

social structure, in their family, in one of their peer groups) explain elements

of their identity formation, ways of psychological or interactional styles, etc.

FORMAL ASPECTS OF LIFE COURSES:

TRAJECTORIES, STAGES,

TRANSITIONS AND EVENTS

We know from research that life course transitions (such as entering or

leaving school, entering professional life or parenthood, marriage, divorce,

retirement, moving into nursing homes in old age) have a great significance

for individual identities and for the connection between people and insti-

tutions. The stages and transitions of life courses are significant in terms of

cognitive abilities, representations of the self, relations with significant oth-

ers and with the institutional and the societal order. Four current concepts

are directly related to any process perspective on the life course: trajectory,

stage, transition and event; they are central for the understanding of human

action, relatively common to various disciplines, and may therefore consti-

tute another inroad to facilitating the construction of interdisciplinary

cooperation.

Trajectory

The meaning of trajectory can be defined as ‘‘model of stability and long-

term changes’’ (George, 1993) or ‘‘sequence of profiles of insertion’’ par-

ticipation (Levy, 2001). In this sense, the notion of trajectory is mainly used

to describe the movements or developments occurring during the whole

span of life, i.e., all that takes place between the two ultimate life boundaries

– or, in our context, transitions – that are birth and death. If this explication

remains especially close to sociological use, one finds a similar one in social

psychology where trajectories are conceived of as all the movements of an

individual in the social space (see among others Viaud, 1999, p. 80) and in

social demography with the notion of biography (Courgeau & Lelievre,


1989, p. 56).11 On a more formal level, we may view trajectories as com-

posed by a sequence of transitions (change) and stages (stability).

Cognitive-developmental psychology proposes an in-depth analysis of the

models of change and stability in the long run (life span). The idea of the

integration of a lifelong trajectory results from the reformulation of the

concept of development. The principle of a definite succession of stages in a

classical structuralist perspective is completed by that of a sequence of

compromises and settlements (growth, maintenance/resilience, regulation)

arising from heterogeneous resources (biological, social, cultural). This,

considered under the angle of individual development (or ontogeny) and

cognitive capacities in particular, contributes to shaping an individual

trajectory.

Baltes and Schaie (1976) discuss cohort effects on cognitive performance.

Such differences were often confounded with age effects. These results jus-

tify the distinction of typical trajectories that correlate strongly with insti-

tutional influences and societal transformations while these may in turn

affect cognitive performance. Baltes (1987) discussed the relative regularity

of processes of change during the first years of life. Baltes interpreted these

results by stating that the social fabric is more solid, substantial and ho-

mogeneous during childhood than during adulthood, the latter showing less

regularity due to a greater variability of situational factors. Moreover, one

could add that the more relevant influences of biological nature during

childhood (maturation, etc.) imply more regularity in that section of the

developmental trajectory.

The shift to a social-psychological and sociological perspective on this

question happens in the first place by considering the principle of ‘‘linked

lives’’ formulated by Elder (1974): trajectories of the individual members of

the same primary group (family, friends, workmates) are interdependent.

For instance and in principle, it is necessary to take into account the tra-

jectory of the father and the mother of any individual to understand the

logic underlying that individual’s trajectory. In sociological research, this

principle is widely applied, notably in research on strategies of social mo-

bility, socialization, domestic functioning, etc. The same principle could

bring important results in interdisciplinary research.

In order to build a more complete model, it is advisable to add institu-

tional, historical and geographical dimensions (Kohli, 1985; Elder, 1985;

Mayer & Schoepflin, 1989; Heinz, 1992) to these individual and relational

factors. Ideally, the ambition of such a global model would be to capture

and interpret the interdependences between these various developmental

dynamics. In practice, however, one often prefers to distinguish specific


trajectories (marital, professional, health-related, residential) within the

global trajectory.12 This raises two important questions: on the one hand we

need to know how to take account of the mutual influences between these

particular trajectories instead of studying them separately. On the other, is it

after all possible and/or desirable to merge the separate trajectories into a

unique global trajectory?

In the perspective of modeling multidimensional trajectories, Abbott

(1992), a historian and sociologist working on both the empirical and the-

oretical level of trajectories, proposes a ‘‘narrative’’ approach where the

notion of narration is substituted to that of causality. According to Abbott,

one of the major problems of research concerning trajectories lies in the fact

that one is mostly confronted with a variety of sequences of events occurring

at different speeds. These multiple temporal horizons constitute a theoretical

barrier that prevents researchers from raising formalized stories beyond the

‘‘simplistic’’ analyses of stage process (stories which develop relatively in-

dependently) and sequences of rational actions. The possibility of matching

sequences of events occurring at various speeds might prove very useful for

life course research. The study of life courses might require the integration

of seemingly quite heterogeneous dimensions in a unique interpretational

model. Ontogenesis does not simply follow a linear succession of ordered

stages. Thus trajectories result from the interaction of various factors of

different kinds, such as biological, psychological, relational and institutional

ones, all probably occurring at different paces and with different impacts on

the life course. This fact raises important theoretical and methodological

questions and surely requires an interdisciplinary collaboration.

Stage

In sociology, demography, social psychology, psychology as well as in other

disciplines, the notion of stage refers to a life period of variable length,

characterized by a relative stability and often something like ‘‘balance’’. At

the very least and on the most abstract level, a stage can be defined as a

‘‘stable state between two transitions.’’ This definition is rather descriptive

and may constitute an empirical cut-out of reality based on the criteria

defined beforehand. Various terms (episode, state, phase, etc.) are based on

a similar logic or similar conceptions across the disciplines. At times, the

duration of a stage offers a better insight.

Jean Piaget has greatly contributed to the definition of the concept of

stage, especially in psychology. He defines stages as relatively long periods


resting on the same underlying structures and equilibrium states.13 In

cognitive developmental psychology, neofunctionalists (or neo-Piagetians)

and contextualists have reconsidered this concept by means of notions such

as ‘‘equifinality’’ (Kruglanski, 1996) and ‘‘testing the limits’’ (Baltes,

Lindenberger, & Staudinger, in press). Neostructuralists have maintained

certain Piagetian principles and added the following modifications: (1) the

redefinition of cognitive structures (other structures than Piaget’s ‘‘logico-

mathematical’’ ones), not necessarily seen as universal; moreover, cultural

and linguistic factors are also considered; (2) a stage is now defined by an

upper cognitive limit, rather than by certain behaviors in different situa-

tions; the upper limit moves during ontogenesis, hence, maturation and

aging are of chief importance; (3) individual differences and characteristic

patterns of individual development are considered; and (4) it is assumed that

affective and social development are similar to cognitive development.

Moreover, the notion of stage has been extended and applied to adult life

(Labouvie-Vief, 1980, 1982; Edelstein & Noam, 1982; Pascual-Leone, 1983;

Riegel, 1976; but see already Erikson, 1950).

In social psychology the concept of stage is used to refer to bases of

retrospective biographical memories (McAdams, 1993, 1999). However, in

many applications of research, the Piagetian conception of stage has been

maintained. Moral development, as conceived by Kohlberg and his follow-

ers (Turiel, 1983; Gilligan & Attanucci, 1988), has kept the universal de-

terministic flavor. However, this conception has been criticized for

theoretical and methodological reasons (Emler, 1999; Tostain, 1999).

In social demography, the concepts of episode or phase are usually used to

refer to well-defined life periods (e.g., employment phases and phases of

professional inactivity for women). Sometimes, the term of age is used to

designate a broader phase of the life course (e.g., adult age).14 However, the

application of the term of stage in a sense close to the Piagetian tradition

seems to be recent (De Bruijn, 1999); its universalistic and deterministic

features are mostly avoided. Instead, emphasis is put on human agency.

Furthermore, the deterministic feature of a stage is opposed to the stochastic

character attached to events.

In sociology, the concept of stage refers to a stable state of various dura-

tions. The social constraints and normative expectations typical of each

stage are generally emphasized. Historically, in a macro-sociological per-

spective, models directly relying on the conception of stage (Comte, Marx,

Spencer, Parsons) have been strongly criticized because of their evolutionist,

linear and deterministic features. In a micro-sociological perspective, the

notion of stage has been applied to the description of family development


and, of course, to individual life courses where it is sometimes used to

designate age-groups (e.g., adolescents, middle-aged, etc.), sometimes also

categories defined by a specific institutional participation (like preschoolers,

employed, retired, etc.). Close terms such as period are often used to avoid

the restrictions of the more assumption-loaded term of stage.

Transition

Across disciplines, the concept of transition refers to the idea of change:

change from one state or situation to another, from one life period to an-

other, from one status or role to another. In this vein and by inverting the

above definition of stage, we might define a transition as the (short) period

of change relating consecutive stages. More precisely and less circularly,

transitions are moments within a particular trajectory characterized by ac-

celerated changes, compared to the relative stability of stages. Examples of

transitions are quite different according to the type of change considered:

change from a limited to a broadened capacity of information processing

(possibly leading from one type of reasoning to another), from single life to

marriage, from adolescence to adulthood, from the status of an employee to

that of a manager. Three characteristics attributed to the concept of tran-

sition seem to be rather consensual across the different approaches: (1) a

transition always refers to its outcome, a novel status of relative stability (in

psychology often characterized by a higher equilibrium or stability, but not

in social demography or sociology); the different approaches, depending on

their objects of study, their objectives and their theoretical assumptions, do

or do not have precise criteria allowing an evaluation of a transition in terms

of the developmental progress it conveys; (2) transitions are processes more

or less clearly limited in time (although they may have long-term conse-

quences); and (3) most often, the concept of transition is applied to the

changes in an individual life course, but it appears also in notions of social

transition, collective transition or even demographical transition.

Defining a phenomenon as transitional cannot be done in an absolute way

since by definition, a transition relates a state ‘‘before’’ to a state ‘‘after’’.

The use of the term depends on the extension and on the specific processes

implied by the transition and may sometimes be a specific way of looking at

an object rather than this object’s inherent characteristics. For instance,

adolescence can either be qualified as a transitional stage, in particular in a

perspective of development from childhood to late adulthood (Durkin,


1995), or as a life stage in itself, with its own developmental tasks, at the end

of which the transition to adulthood takes place (Erikson, 1950).

Three related concepts, each referring to a particular kind of transition,

are: (1) ‘‘revolution’’ (Mounoud, 1982), which refers to a discrete transition,

to the emergence of a new ‘‘structure d’ensemble’’ and thus does not apply

to the acquisition of a new isolated capacity; (2) ‘‘turning point’’ (Gotlib &

Wheaton, 1997), which refers to a transition (or perhaps more frequently to

an event) that implies a change in the orientation of a trajectory and not just

a mere confirmation of this trajectory by a transition that fits into a general

pattern; and (3) ‘‘social mobility’’, upward, downward or horizontal, which

is only related to transitions that affect the social status of a person, mostly

implying an increase or a reduction of his economic, social or cultural cap-

ital (in the case of vertical mobility).

A number of theorists have attempted to apply to the study of individual

transitions the mathematical ‘‘catastrophe theory’’ (Thom, 1975) and the

concept of ‘‘bifurcation’’ (e.g., van der Maas & Molenaar, 1992). Such a

modeling approach has the advantage of combining quantitative with qual-

itative changes. For instance, a series of small quantitative changes can

eventually lead to an abrupt transition, resulting in what proves to be

qualitatively very different from the preceding periods.

Although commonalities of the concept of transition exist across disci-

plines, each discipline differentiates its approaches to and uses of the notion

of transitions. Social demography and sociology share a focus on the study of

the effects of various parameters – socio-demographical (e.g., fertility, life

expectancies, migration, social and geographical mobility) or institutional

(compulsory school, labor market, retirement system, etc.) on the occur-

rence, timing and variability of transitions (Hogan, 1981; Rindfuss,

Swicegood, & Rosenfeld, 1987; Settersten & Mayer, 1997). In social psy-

chology, the same transitions are most often conceived of as independent

variables whose effects are studied (e.g., in studies interested in the effects of

particular transitions on social representations (Viaud, 1999), and self-

concept (Hagestad & Neugarten, 1985; Havighurst, 1972; Kling, Ryff, &

Essex, 1997)). Developmental cognitive psychologists are more interested in

explaining the mechanisms and explanatory factors of transitions charac-

terizing the individual cognitive development (de Ribaupierre, 1989). In a

Piagetian perspective, these mechanisms are described as depending on

processes of maturation, cognitive conflict, equilibration and the interaction

with the social environment. Here, transitions are usually conceived as de-

pendent variables and at a more microscopic level than in sociology or social

demography.


The idiosyncrasies of the various disciplines have not hindered interdis-

ciplinary work. For instance, some researchers in social psychology are

interested in life choice orientations (which directly influence normative

transitions) through the analysis of the impact of values or of social iden-

tities on transitions considered as dependent variables (Cinnirella, 1998).

Furthermore, acknowledging the role of the social environment and the

individual behavior on cognitive transitions requires that the disciplines

interact and provide complementary contributions to the study of the life

course. In this regard, Doise and Mugny (1997) illustrate the facilitating role

of social interactions and social marking in the resolution of Piagetian tasks.

Indeed, this line of research provides insights into the influence of social

dynamics on the transition from one cognitive stage to another, and it also

shows how these dynamics can be studied with an interdisciplinary ap-

proach. Another possible link between social psychology, sociology and

social demography is their common interest as to how transitions are reg-

ulated by normative representations as well as by life experiences and their

interpretation on the one hand, and on the other how these are shaped by

changes in their socio-historical background (Neugarten, Moore, & Lowe,

1965; Elder, 1974).

Event

According to a general definition, an event is ‘‘what happens at a given time

in a given place’’ (Encyclopaedia Universalis, 1999). This notion is ambig-

uous since some events are characterized by their singularity and unexpect-

edness, others by their regularity and expectedness. This ambiguity holds for

several events of the life course. For instance, life events (such as a second

marriage) can be considered as unique at the level of individuals. They ‘‘are

particular moments in a particular time and place, complete with particular

characters, actions, thoughts and feelings’’ (McAdams, 1993, p. 258). But

they can also be characterized by their repetition at the level of a social

group or a population. In this case, the event could be defined as a ‘‘fact

which concerns an individual and which affects the structure of populations

and its development’’ (Pressat, 1979, p. 68). Scientific disciplines could be

distinguished depending on the emphasis they give either to the singularity

or to the expectedness of an event. In this way, one can oppose, e.g., socio-

demographical and socio-psychological approaches.15

Social demography emphasizes regularity (i.e., repetition). This is achieved

by: (1) describing the event (e.g., marriage, birth, death); (2) considering this


event as part of a collective phenomenon (e.g., nuptiality, natality, mortal-

ity, mobility and migration); (3) using quantitative methods for this de-

scription; and (4) eventually predicting the event with the tools of

demographic projection. The meaning of the event is defined on the basis

of its statistic regularity. For instance, in the frequently cited paper titled

‘‘The changing meaning of cohabitation and marriage’’ of Manting (1996),

the meaning of cohabitation or marriage in the Netherlands is not derived

from Dutch values and opinions about these events, but from the devel-

opment of partnerships starting as cohabiting unions and marriages of co-

habiting couples. Entry into cohabitation was rare during the 70 s, and

cohabiting couples rarely married. However, this kind of union became

more and more frequent during the 80s and 90s, as cohabiting couples

married more often. According to Manting, the role of cohabitation evolved

from a substitute to marriage to a preliminary period before marriage.

Social psychology insists on the singularity of events. This is achieved by

assuming that a person defines an event and then by focusing the research

on the possible disturbance brought about by the event on the individual

identity. The event represents a mark between a ‘‘before’’ and an ‘‘after.’’

Because the event is defined by the individual (or the group), social psy-

chology insists on its subjective character. For example, the concept of

attribution captures the idea that various degrees of causal relations could

be established between behaviors and ‘‘reinforcements’’ (e.g., the interpre-

tation of an event as positive or negative; Deschamps & Beauvois, 1996).

Attribution leads to the notion of allocation (i.e., how persons can explain

events in their everyday life). Here, a distinction is made between ‘‘internal

and external causalities.’’ Internal causality means that an individual at-

tributes the event to him- or herself and external causality that he or she

names an external, environmental cause for the event. The current hypo-

thesis is that the type of attribution performed by the individuals has major

consequences on their future behaviors.

Sociology and social demography have a certain extent of common ground

as far as the definition of events is concerned, especially of events perceived

by the individuals as intrinsically motivated (e.g., marriage). Furthermore,

sociology also insists on the expectedness of the event (normative life events

vs. stressful events). In the case of stressful events, the interest lies especially

on their ‘‘consequences’’ for the life courses.

In cognitive psychology, the event is mainly apprehended as a stimulus or

a novel phenomenon or a disturbance the individual has to react to, and is

thus usually defined by the experimenter (i.e., simulated and manipulated as

an independent variable). Typically, the research is not interested in the


event itself, but in the individual reaction to the event understood as a

stimulus.

The meaning of the term ‘‘disturbing event’’ is sometimes only slightly

different in each discipline. In social demography, the succession of events is

addressed by most researchers. The questions circle around the effects of a

perturbing event on the occurrence of other events (e.g., the effect of mi-

gration on marriage; Courgeau & Lelievre, 1989). In this perspective, dis-

turbing events are similar to the ‘‘turning points’’ in social psychology or

sociology. These events coincide with trajectory changes (‘‘bifurcation’’ as

described in sociology by de Coninck and Godard (1989) is a closely related

concept). An example of a turning point (or event) is a disabling illness or

accident and its eventual consequences in the form of a deep and global

reorientation of life. Events occurring in a ‘‘coherent’’ way within the overall

orientation of a trajectory can be opposed to turning points. In sociology

and social psychology, disturbing events are unexpected and often con-

sidered as ‘‘stressful’’, at least potentially. These events can be directly re-

lated to persons (e.g., illness) or to external events (e.g., death of a

significant relative, parents’ divorce, collective dismissal). In this last case,

the interest lies in the amount of stress caused by the event depending on its

social context and meaning.

In the present context, a special mention should be made of the research field

of life event research (Dohrenwend & Dohrenwend, 1982; Brown & Harris,

1989), an area of study that rests on the basic hypothesis that unforeseen events

in the life course can alter its further progress and have more or less important

consequences on the persons who experience them. It is multidisciplinary since

at least social psychiatry, social psychology and sociology are being mobilized

by life-event researchers, and it represents one extreme, ‘‘empiricist’’ option of

interdisciplinarity in that it is often practiced with particularly little theoretical

preconceptions, at least concerning the nature of significant events and of the

mechanisms that relate them to their possible effects on the life course. Even

though it seems to be rarely integrated by more outspoken life course or life-

span-oriented research, some results of this line of research confirm basic tenets

of life course research precisely because of its atheoretical conception, such as

the finding that the positive or negative value of life events cannot be found in

themselves, but depend strongly on their interpretation by the concerned actors

and their environment, that this evaluation depends on the coping resources

concerned actors can mobilize, and that the abruptness of their occurrence

may be as consequential as their qualitative noxiousness (which reminds one of

Durkheim’s notion of ‘‘happy crises’’ that may create, according to his

hypothesis, as much anomy as unhappy ones).


A disturbing or stressful event may act as a turning point. In a rigorous

conceptual perspective, we may even ask whether the notion of events

should be located on the same level as the other three terms, trajectory, stage

and transition. We might also consider events to be potential transitions (of

positive or negative social value) depending on the possibilities of life course

actors to cope with them. Finally, what an event is may vary as a function of

the fine-grainedness we choose for our interrogation: divorce can be con-

sidered as an event, unique and momentary, e.g., in a demographic analysis.

But it can as well be studied as a rather long-term process in which the

formal act of legalized separation is only one relatively late step, and maybe

not even the most consequential one.

TRANSVERSAL SUBSTANTIVE THEMES: CONTENTS

OF THIS VOLUME

A third line of interdisciplinary collaboration can be found in transversal

themes or research dimensions that appear in several disciplines, even

though they are typically approached differently by them. In a way, the

formal aspects treated in the previous section – especially transitions and

events – have also very substantive meanings; they can therefore also be

subsumed under this heading and may be considered as first examples.

Examples of other such dimensions are, in an arbitrary and imperfect order,

gender differentiation, subjective vs. objective aspects of the life course and

their relationships (‘‘subjective’’ including questions of experience, repre-

sentations, identity, projects, cultural models, etc.), agency vs. structure in

and about life courses, linked lives, interinstitutional links forming life

course regimes. We may also think of thematic fields such as gerontology,

which, by definition, is inter- or at least multidisciplinary. Various academic

departments (especially human development and family studies) as well as

centers of gerontology typically count among their member scholars from

different disciplines, such as demography, sociology, psychology, biology,

kinesiology, nurse practice, health policy, communication science, social

work, educational sciences, ecology, counseling, anthropology, philosophy,

theology, etc.16 It is, however, certainly not sufficient to have different dis-

ciplines attached to the same organizational unit for interdisciplinarity to

develop, even if this can become a favorable condition. Direct collaboration

is a much more promising situation, especially in projects explicitly designed

to be interdisciplinary from the outset.


We have chosen to organize this volume around four such themes: agency

and structure, transitions, biographical reconstruction and methodological

innovations. The first three themes are substantive and take up some of the

transversal lines we have sketched earlier in this essay. The disciplinary

origins of their authors reflect that they are not yet equally treated in the

disciplines we solicited for this volume.

Agency and Structure

Agency and structure has become an important issue in sociological debates

since the 1980s, perhaps less so in other disciplines. The first chapter takes

up this theme, locating it clearly in the substantive area of life course

research.

Settersten and Gannon focus on the joint impact of social structure and

human agency, in proposing a life course model of agency within structure.

They consider how individuals actively create their own lives and maximize

their own development within parameters set by their social worlds (some of

which may constrain them, and some of which may enable them), and how

individuals interact with, and even make proactive attempts to alter, those

worlds. Several examples from three different life periods are provided:

childhood and adolescence, early adulthood through midlife, and old age.

The tension between the theses of standardization or destandardization of

life courses is reconceptualized as not to be resolved by one thesis winning

out over the other, but as being acknowledged to be complementary proc-

esses, loosely associated with agentic and structural dimensions of the life

course.

Marshall tackles the topic of structure and agency first by reviewing the

literature for explicit and implicit definitions of these two crucial terms and

by revisiting, in an autobiographic return, some earlier results of his com-

parative study of aging communities. He comes up with a theoretical model

containing both aspects and recovers Clausen’s concept of planful compe-

tence as one straightforward way to conceptualize life course agency. Like

other contributors, he reminds us of important complexity that must not go

unnoticed in life course research, especially a necessary differentiation of the

concept of identity, and expresses hope for a rejuvenating theoretical input

by European life course researchers.

Luscher looks into a specific and often neglected aspect of social relations,

especially intergenerational relations, tending to rehabilitate ambivalence as

a basic psychological ingredient for sociological analysis. This is a timely


hint at the fact that the links between the lives life course analysis is bound

to take into account are not just arrows on network charts, but real social

relations with their affective depth and complexity. In fact, the concept of

linked lives is extremely important, but still awaits theoretical and empirical

development, not only concerning the type of people that can lead linked

lives (like successive generations or partners in a couple), but also referring

to the nature of their relationship (besides the emotional valence, their

strength is also a variable that merits more consideration: are linked lives

based only on strong ties or may weak ties also play this role?).

Transitions

While life course research makes a strong argument for considering the

whole life span instead of limiting its interest to specific parts of it (especially

single phases), specific aspects of life courses may be more fertile for re-

search than others. Transitions are particularly revealing of life course dy-

namics on several accounts and are at the center of four contributions.

Although, for the time being, they seem to be principally investigated by

sociologists, they look promising for psychology and social psychology as

well.

Mortimer and her co-authors attempt to disentangle the respective influ-

ences of social structures and individual agency, in focusing on the issue of

educational attainment in the transition to adulthood. Some scholars high-

light cultural values, normative timetables, stratification processes and in-

stitutional career lines as determinants of the contents and pacing of role

changes through the life course. Others, in contrast, emphasize the exercise

of human agency as a central causal force in shaping the life course, in-

cluding the expression of values and identities, self-regulative processes,

decision-making and striving to achieve personal objectives through goal

selection, strategic planning and action. Mortimer and colleagues focus on

the interactions existing between these two dimensions. Using data from the

Youth Development Study, a 15-year panel study of work experience and

the transition to adulthood, they show that early goals and values are linked

to work behavior during high school, which in turn has predictive power

with respect to subsequent trajectories of work, schooling and educational

attainment. The study presents promising ways for opening the ‘‘black box’’

of the processes and mechanisms, both structural and agentic, underlying

life paths of transition to adulthood.


With an autobiographical twist, Furstenberg takes up the thesis of many

life course researchers that chronological and sequential ‘‘order’’ is impor-

tant, that deviation from this order (like being out of schedule) constitutes a

problem and is bound to provoke undesirable outcomes. Reviewing recent

analyses, he shows that results seeming to confirm this somewhat structuro-

deterministic idea are in fact less than convincing and remain open to

contrary, more optimistic and ‘‘agentic’’ interpretations; according to his

argument, ‘‘life course deviance’’ may as well lead to growth in life course

managing capacities. A major role in determining whether non-normative

trajectories, be they out-of-time or out-of-order, become problematic for

later stages in life seems to be reserved to social and psychological resources

and thus index the structural environment of ‘‘life course passengers’’.

Looking into The Secret of Transitions, Bird and Kruger develop a strong

argument about the danger of substantive blinders brought along by meth-

odological and conceptual reductions, for example, in the case of the study

of life course transitions by help of event-history analysis. They advise us

not to forget the complexity of life courses in the double sense that they are

made up of several parallel and related trajectories and that transitions

belonging to different trajectories may interact without necessarily being

synchronized. Moreover, even in one trajectory, transitions are often multi-

layered and last longer than their treatment as ‘‘events’’ would suggest. On

the basis of empirical examples concerning sex-differentiated life courses,

they propose a timely typology of transitions based on their socio-dynamic

features that can help avoid excessive reduction of social complexity on the

conceptual and interpretive level.

Biographical Reconstruction

Life course and biographical analysis (in the sociological, not the demo-

graphic use of the term as we pointed out before) have long been considered

to be strangers ignoring each other – if not hostile antagonists – especially in

some European research traditions. Heirs of the fundamental debate be-

tween the ‘‘two cultures’’ in the social sciences, life course analysis is often

purported to be positivist, explanatory and quantitative; biographical anal-

ysis constructivist, interpretive and qualitative. While this division still ex-

ists, it has been questioned by more recent pragmatic positions, preferring

mixed methodologies (Tashakkori & Teddlie, 1998) and quali–quanti tri-

angulation (Erzberger & Prein, 1997) to methodological fundamentalism.

Even though this debate has probably raged more in sociology than in most


other social science disciplines, it is highly relevant for all of them, especially

in the life course area. The contributions concerning biographical recon-

struction are instructive in this respect: the first relates identity changes

directly to life course transitions, the second approaches directly and em-

pirically the crucial theme of biographical memorization and the third is an

example of interpretive work in a discipline that has heretofore privileged

quantitative approaches. Biographical accounts have become a field more

currently shared at least by social psychologists and sociologists.

In a chapter relating this part of the volume to the previous one, Emler

presents in a social psychological perspective how identity shifts take place

during life transitions. A first section is devoted to the presentation of the

classical view of cognitive development (Piaget, Kohlberg) as abrupt qual-

itative changes resulting in a succession of qualitatively different stages.

Then social identity is defined on the basis of self-categorization theory,

taking into account the relationships between social identity and social net-

works. One central issue of this chapter is to understand if identity devel-

opment is a gradual process or if it corresponds to a stage model with

sudden and discrete changes. Based on different examples in the areas of

political identity and changes in personal relationships, it opens new direc-

tions of thought and research in the study of shifts of identity across life

transitions.

Perrig-Chiello and Perrig bring together different important psychology

fields in the study of personality across the life span: autobiographical

memory, well-being and personality traits. Psychologists have developed

different theories on how the individuals construct their autobiographical

memory. This chapter presents empirical evidence showing that personality

traits like neuroticism and extraversion have a consistent relationship with

recollection of autobiographical episodes and well-being. Results also in-

dicate that the impact of personality traits may be more important than

positive or negative life events. These results raise important questions

about how individuals adapt and reconstruct life events across their life

course, and how subjective experiences, and in particular personality traits,

may have objective effects on life trajectories.

McAdams is a personality psychologist who has developed a narrative

approach to identity across the life span – a highly welcome complement to

the personality trait approach. Building on the idea that identity is con-

structed in a continuous process across the life course, he shows that life

stories are developed by individuals following distinct ‘‘story lines’’. For

example, he shows that life stories may be narratively organized as

contamination (good to bad) or redemption (bad to good) sequences.


The various antecedents and consequences of these types of life stories are

described on the basis of empirical examples. A promising development of

this approach is the possible link between these life stories and the cultural

context in which they are expressed and constructed.

Methodological Innovation

Methods of analysis have a peculiar role in our thematic field, especially so

in quantitative methods.17 They are not very numerous and well-known and

they are based on technical and also conceptual implications that have to be

scrutinized all the more seriously for their implicit assumptions and basic

logics for the interpretation of results as they are practically all imported

from more or less distant fields or disciplines (this is probably the most

multidisciplinary sector in life course research!). Moreover, the danger of

one method dominating the field and the substantive outlook of researchers

merits particular attention (one obvious candidate for becoming a ‘‘he-

gemonic method’’ in quantitative life course research is event history anal-

ysis, with optimal matching a new-coming junior competitor that remains at

the margins of the arena for the time being). Therefore, we find it useful to

contribute to methodological diversification with a set of promising and

innovative contributions presenting methods for exploration and analysis

that have not yet made decisive inroads into this area. The three chapters of

this volume nicely complement each other, largely due to the different, but

sometimes converging and highly complementary, research fields of the

authors. Francesco Billari’s applications are mainly in demography, espe-

cially in the research fields of fertility and transition to adulthood. Michel

Oris, a demographic historian, and Gilbert Ritschard, an econometrician,

have been collaborating for several years on projects requiring both an acute

historical sense for the analyses of demographical issues and advanced an-

alytical tools, which statistically address the theoretical questions of interest.

The work of Jack McArdle, a psychologist, has been focused on age-

sensitive methods for psychological and educational measurement, and lon-

gitudinal data analysis. Together, the four authors provide an exceptional

overview of novel life course methodologies of great potential and invite us

to think about future developments. Each chapter briefly summarizes the

state of the art of its research field before heading into the most recent

advances and promising extensions.

In his contribution, Billari compares the two major approaches of

quantitative methods used in life course research, the event-oriented and the


holistic approach. The former quantitative approach is largely represented

by the freshly popular analytical techniques of event history (or survival)

analysis and also newer techniques stemming from program evaluation work.

The latter approach is mostly represented by techniques used to analyze any

sequence of events and trajectories over the whole life course, and the prime

among these is optimal matching analysis. Both approaches are used in turns

to answer different but complementary life course research questions. The

event-oriented approach is often used in a causal perspective of analysis, and

the author describes all different kinds of causal factors susceptible to affect

the occurrence of an event. The holistic approach is adopted to analyze

different factors influencing trajectories. Billari then invites the reader to

consider that the two perspectives are couched in the two statistical cultures

of data vs. holistic modeling discussed in the very influential paper of

Breiman (2001). The data modeling culture naturally pairs up with the

event-oriented approach, the algorithmic modeling culture with the holistic

approach. Billari provides several examples to clarify his arguments and to

illustrate how the two traditions and approaches complement each other in

addressing life course phenomena.

The chapter by Ritschard and Oris discusses how the fields of historical

demography and contemporary demography promisingly joined forces, es-

pecially under the life course research paradigm. They discuss three ana-

lytical approaches and illustrate them by relevant examples. First, event

history analyses are again taken up, but this time in relation to very recent

advances allowing to account for shared heterogeneity or frailty in the

presence of multiple groups. The combination of event history analysis with

multilevel modeling will undoubtedly witness much success in several re-

search fields. The second set of statistical techniques presented by the authors

is that of Markov transition models used to analyze state sequences, or in-

dividual trajectories across quantitatively and possibly qualitatively different

phases in the life course. The categorical changes can be dependent of ob-

served or latent covariates and differ with respect to their temporal expres-

sion, or lags. Again, this technique is progressing at a quick rate and the range

of its applications is growing accordingly. The third set of analyses consists in

longitudinal data mining techniques based on an induction tree approach.

While more exploratory in nature than the previous two techniques, induction

trees ideally describe the change trajectories of complex multivariate systems,

in which the elements are ordered by importance and predictability power. As

illustrated by the authors, this newer set of techniques may complement the

other two and add much insight into life course research objects, over and

above what was already learnt with more classical techniques.


McArdle accompanies the reader in exploring a unique data set of cog-

nitive measures obtained on seven occasions from 1931 to 1998 on the same

individuals. The data are first described and observed with respect to their

presence as well as absence (i.e., incomplete data). Then individual and

group change characteristics are determined and possible predictors of

change are tested. Latent growth curve models, a particular application of

structural equation modeling, are used to this end, which are known in the

educational and biostatistical literature under different names (hierarchical

linear modeling, multilevel modeling, random or mixed effects modeling, etc.).

Group differences in developmental trajectories are then explored, on an a

priori level (via traditional multigroup structural equation modeling) as well

as on an a posteriori level (with more recent latent mixture models). Finally,

McArdle introduces an advanced class of structural equation models based

on previous work in econometrics, biostatistics and behavior genetics to

explore dynamics of change. In other words, we are now invited to examine

the data in a non-static perspective, going from description to explanation of

change phenomena. The advanced latent difference score models introduced

by McArdle represent a very promising set of analytical tools capable of

addressing classical as well as innovative theories of change in several

research fields.

Finally, in their afterthought chapter, the editors bundle together the

novel impulses for fostering interdisciplinary life course research that can be

gleaned from the contributions to this volume and suggest directions for

future development.

NOTES

1. The Pavie Team, as it existed when the interdisciplinary venture that producedthis volume was launched, comprised a founding group of six professors and eightresearchers and research assistants. They all participated in the elaboration of aworking document that was the basis of an international colloquium held in October2003 and that has been largely integrated into this introduction. In order to under-score the team character of this work, we list their names in strict alphabetical order:Jean-Claude Deschamps, Guy Elcheroth, Yannic Forney, Jacques-AntoineGauthier, Paolo Ghisletta, Jean Kellerhals, Christian Lalive d’Epinay, Jean-MarieLe Goff, Rene Levy, Anik de Ribaupierre, Claudine Sauvain-Dugerdil, Dario Spini,Manuel Tettamanti, and Eric Widmer.2. Coming from two – at times – different scientific traditions, the terms life course

and life span share so much meaning at many levels (conceptual and methodological)


that we can safely focus on the similarities between the two terms and consider themas basically synonymous. Instead of using them interchangeably, we rather settle onthe use of life course for the sake of simplicity (for a discussion of these two terms,along with the less adequate one of life cycle, see Settersten (2003a) who takes thesame terminological stand as we do).3. An entirely parallel argument could be made for psychology and probably

other disciplines of the social sciences.4. One can lead lengthy discussions about the proper use of terms like macro-

scopic or microscopic – what is micro for a sociologist may be very macro for apsychologist, what is micro for a psychologist may be way underneath the systemiclevel for which other social sciences have analytical resources. Instead of doing this,let us just illustrate the notion of encompassing system levels by citing three examplesin ascending order: different psychological systems (like memory, emotion, etc.) –interindividual interactions – relations between social institutions (e.g., in organizingsubsequent or simultaneous stages of the life course).5. This is an uneasy translation of the standard French expression ‘‘rapports

sociaux de sexe’’.6. It goes without saying that a similar argument could be made for all classical

sociological variables supposed to capture some aspect of social differentiation, firstof all ‘‘class’’ or position in social hiearchies, but several of these are socially less‘‘thick’’. We do, of course, not mean to relegate them to a secondary position foranalysis, but rather feel that in current research, there is a danger of too exclusive aconcentration on them.7. The most general paradigm of this sort in sociology is rational choice theory or

‘‘methodological individualism’’. But there are many more specialized areas wherepredominant approaches at least implicitely adopt an individualist stance, for in-stance in the form of the status attainment paradigm in mobility research. In psy-chology, the same problem exists but in a more fundamental sense, considerations of‘‘contextuality’’ being more intrinsically out of theoretical bounds than, say, insociology, social psychology or political science (but see Bronfenbrenner’s ecologicalmodel (Bronfenbrenner, 1979), Baltes’ contextualism (Baltes, Lindenberger, &Staudinger, in press), or even Erikson’s epigenetic model (1950).8. There are some special research fields where interdisciplinarity is already pract-

iced in a more or less regular way, such as gerontology. But even here, collaborationis often more of a coordinated parallelism than real cooperation. Moreover, in thiscase, research is not systematically placed in a full life course framework.9. Some authors suggest that the vocabulary of truly transdisciplinary analysis

should be based outside the boundaries of scientific terminology. This may be usefulin a highly problem-oriented research, but should not be seen as a general rule.Systems theory provides a scientific vocabulary for transdisciplinary conceptual-ization which is, to the least, not less promising than everyday or other non-scientificlanguage.10. The current French expression is ‘‘analyse demographique des biographies’’

and has nothing to do with the qualitative study of biographical reconstruction thatsociologists might have in mind.11. We should note that in some contexts, as here, ‘‘biography’’ is practically

synonymous of ‘‘life course’’ whereas in others, the distinction of these two concepts


is almost as important as that of sex and gender in gender studies, with ‘‘biography’’meaning the narrated, principally self-accounted history of an individual, and ‘‘lifecourse’’ the factual unfolding of life as it can be registered by an outside observer(e.g., the Research Committee Nr. 38 ‘‘Biography and Society’’ of the InternationalSociological Association is clearly interested in biographical research in the sense ofthis distinction; cf. Rosenthal & Kottig, 2004).12. One can distinguish the interdependences between processes belonging to dif-

ferent spheres in the life of an individual, or between processes which concern anindividual and his significant others, or a process which concerns an individual and atransforming context.13. In most disciplines, the concept of stage, even if conceived as an element of a

continuous process, is not inherently linked to notions of finality and universalityalthough these notions have often been intimately associated with the definition ofstage. This misconception has been strongly criticized, especially by anthropologists.An important example of such a debate about implicit and scientifically unfoundeddirectionality assumptions on a supraindividual level has concerned modernizationtheory.14. For some researchers interested in fertility issues, the reproductive age (career)

might be divided in several phases or stages, each of them characterized by a specificknowledge, a childbearing motivation, and a style of decision making (Forrest,1988): (1) before first sexual intercourse; (2) from first sexual intercourse to marriage;(3) from marriage to first birth; (4) first birth to desired family size and (5) fromfamily completion to mother’s menopause.15. We do not explicitly integrate the specific area of life-event research that has

developed mainly in social medicine and psychiatry, but would like to mention it asan interesting source of empirical findings that strongly underscore the relevance ofspecific and especially of singular events for subsequent periods of life. At the sametime, the diversity and sometimes even contradictory characters of these findingspoint to the necessity of explicitly taking into account conceptual tools of variousdisciplines, particularly with respect to the non-reductible complementarity of ob-jective and subjective features (i.e., their personal meanings) of such events if we areto understand their consequences.16. This is often possible because faculty members hold joint or courtesy ap-

pointments. Hence, they belong to distinct departments and only occasionally col-laborate on common projects. This sort of institutional structure, althoughrepresenting progress, is not the ideal environment for fostering common researchgrounds. The sharing of information is undoubtedly facilitated by centers of thiskind. However, interdisciplinarity, as we claimed before, aspires to deeper linkagesamong the disciplines. This is reflected by the lack of Ph.D. programs in gerontology.To our knowledge, various postgraduate diplomas exist in gerontology, but only fewPh.D. titles; the same seems to hold for life course studies.17. Given their greater openness, qualitative methods do not seem to present the

same potential of substantively orienting research and of blinding it toward aphenomena for which the methods we use have no outright provision. Even though,a similar danger could be related to an exclusive use of qualitative methods, espe-cially if accompanied by a conceptual reduction of the range of research questions tothose qualitative methods can answer.


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PART I:

AGENCY AND STRUCTURE

33


34

STRUCTURE, AGENCY, AND

THE SPACE BETWEEN:

ON THE CHALLENGES AND

CONTRADICTIONS OF A BLENDED

VIEW OF THE LIFE COURSE

Richard A. Settersten, Jr. and Lynn Gannon

The field of life course studies has at its core two propositions for which

there is an inherent tension: one emphasizing that the life course is the

product of social forces (broadly construed as ‘‘social structure’’), and the

other emphasizing individual capacities and effort (broadly construed as

‘‘human agency’’). A wide array of perspectives on structure and agency can

be found in the literature. At one extreme are models of structure without

agency. More common in the discipline of sociology and in European

scholarship, these models take the life course to be largely constrained, if not

determined, by the characteristics of and processes in social settings, and by

the locations of individuals within those settings. Politically, these models

can be viewed as problematic, at least if they are carried too far, because

they ‘‘externalize’’ blame and leave little or no room for personal respon-

sibility.

At the other extreme are models of agency without structure. More com-

mon in the discipline of psychology and in North American scholarship,





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10001-X

35

these models take the life course to be largely fostered, if not determined by,

individual decisions and actions. Life, as the adage goes, is largely what one

makes of it. While few of these latter models take the life course to be

completely devoid of social obstacles and barriers, these models often

downplay the effects of social forces and assume that good planning and

hard work go a long way in overcoming barriers. In the political sphere,

these models can be dangerous, at least if they are carried to an extreme,

because they blame people for negative outcomes and suggest that individ-

uals need not be extended support from the state because their problems and

circumstances are their own doing.

In both cases, these models have led to the question ‘‘What matters more,

structure or agency?’’ and to discussions of agency versus structure. While

these two prevalent perspectives offer different and important lenses for

understanding human development, a third and underdeveloped set of

models instead seem necessary to advance interdisciplinary life course re-

search. These are blended models of agency within structure, which explicitly

seek to understand how individuals set goals, take action, and create mean-

ings within – and often despite – the parameters of social settings, and even

how individuals may change those parameters through their own actions.

These models bridge over-structured and under-structured views of the life

course by asking how the characteristics of and processes in social settings

interact with the characteristics, capacities, and actions of individuals to

jointly affect life trajectories and outcomes. They involve incorporating in-

teractions with a wide range of social settings both near to and far from

individuals – from proximal settings of everyday life such as families, peer

groups and friendships, neighborhoods, schools, or work organizations, to

more distal settings such as the labor market, the state and its policies, and

historical events and periods of social change.

Modes of agency within structure bring significant challenges because

they demand that boundaries between disciplines be crossed, especially be-

tween life-span psychology and life course sociology; that the concepts and

measures of ‘‘structure’’ and ‘‘agency’’ be clarified; and that more sophis-

ticated theories and methods be developed to frame and analyze them.

Models of agency within structure also demand a critical evaluation of the

unique nature and effects of structure–agency dynamics within and across

distinct life periods. The tensions between structure and agency need not be

resolved as much as capitalized upon to build new social theories and re-

search on specific life periods and on the life course as a whole.

Finally, there is mounting and conflicting evidence that the life course has

become both more standardized (with regularity in life course patterns being

RICHARD A. SETTERSTEN, JR. AND LYNN GANNON36

driven by the increased ‘‘institutionalization’’ through norms, laws, and

social policies) and de-standardized (or ‘‘individualized,’’ with variability in

life course experiences being driven by greater freedom to ‘‘live a life of one’s

own’’). The chapter ends with some thoughts on how these contradictions

may be explained by more explicitly tending to structure–agency dynamics.

STRUCTURE, AGENCY, AND THE SPACE BETWEEN

Structure

The concept of social structure is difficult to grasp and make precise. The

things indexed by the notion of social structure are recognized as the central

sociological contribution to the study of lives, yet there is little agreement

about what exactly structure is (see also Alwin, 1995). Indeed, sociologists

not only find it difficult to define structure adequately, but they often cannot

do so without using the word ‘‘structure’’ or a variant of it (see also Sewell,

1992). Conventional approaches treat structure as a powerful set of stable

top-down forces that impinge upon individuals and cannot be (easily) al-

tered. As an example, consider Alwin’s (1995, p. 218) definition of social

structure as a set of ‘‘opportunities and constraints within networks of roles,

relationships, and communication patterns, which are relatively patterned

and persisting (emphasis added). These opportunities and constraints may,

at one extreme, refer to ‘‘large, organic institutional structures, such as

bureaucracies, which structure and orient human activities,’’ or they may, at

the other extreme, refer to a set of ‘‘dyadic norms negotiated between two

individuals for the purposes of social exchange.’’ This definition of social

structure is typical in that it emphasizes stability, but it is unusual in that it

acknowledges both constraints and opportunities, and both macro- and

micro forces.

The emphasis on stability, however, has led to the neglect of dynamic

aspects of structure. Few approaches have emphasized the fact that people

have the ability to change structures (that is, that the relationship between

people and structures is reciprocal) and that both social structures and

human lives and the connection between them, are dynamic (an important

exception to this trend has been the age stratification framework long ad-

vocated by Riley and colleagues; e.g., Riley & Riley, 1999). On the surface,

the notion of ‘‘dynamic’’ structures seems to negate the very concept of

structure, which has stability at its core. But in a fast-paced world of rapid

social change, it may be transformations in social structure, and the po-

Structure, Agency, and the Space Between 37

tentially disruptive effects they have on human lives, that pose new chal-

lenges for growing up and growing older today.

Sociologists of ‘‘social stratification’’ have emphasized the fact that the

social structure of a society can be described through key dimensions of

social organization: age (or cohort), race (or ethnicity), sex (or gender), and

social class (education, occupation, income, or some combination thereof).

Some of these are ‘‘ascribed’’ statuses into which individuals are born or

over which individuals have little control. Others are ‘‘achieved’’ statuses

that are largely the result of performance, effort, or things over which in-

dividuals presumably have some control. Much social thought related to

stratification has often been tied to the question of who gets what in society.

In most complex societies, certain individuals and groups hold dispropor-

tionate shares of social resources (e.g., property, power, prestige), and so-

ciologists have traditionally been interested in how and why the distribution

of resources varies as a function of the dimensions noted above.

Similarly, both life course sociology and life-span psychology continue to

be dominated by what Bronfenbrenner (1988) once called ‘‘personal at-

tribute,’’ ‘‘social address,’’ and ‘‘social niche’’ models. Personal attribute

models group and compare individuals based on biological or physical fea-

tures (e.g., age, sex, body type). Social address models group and compare

individuals by geographical or social group (e.g., urban or rural, social class,

race or ethnic group). While social address models focus on geographical or

social dimensions, they nonetheless often rely on personal attributes as a

means for grouping individuals. Social niche models group and compare

individuals based on intersections between multiple statuses (e.g., poor,

young, unmarried mothers versus other groups).

All of these models can be described as class-theoretical models because

they assume that the characteristics of individuals serve as important proxies

for ‘‘social structure’’ and individuals’ experiences in it, that they index some

aspect of inequality, and that the phenomena under study are somehow

explained by the categories themselves. In reality, however, these variables

provide little or no direct information about the characteristics of or proc-

esses in the social worlds that individuals inhabit, nor about the experiences

of individuals in those worlds. When investigators find significant differ-

ences between classes, they are then faced with the challenge of having to

explain these differences – and it is with respect to explanation that class-

theoretical models are not sufficient. Because these models are based on the

characteristics of individuals, they do not tap what most sociologists and

ecologically minded psychologists think about as social structure. A big and

problematic leap of faith is required when we assume that individuals who


share a set of personal characteristics also share similar social worlds and

experiences. At best, such a leap leads to suspect interpretations; at worst, it

results in misleading and false ones. This is especially true when class-

theoretical approaches are used to explore what Bronfenbrenner calls field-

theoretical questions – that is, when we want to understand the ecology of

human development.

Because ‘‘social structure’’ is such an elusive concept, it is not surprising

that its measurement and modeling are so difficult. Important strides must

be made in conceptualizing social settings, measuring their characteristics

and the processes that occur in them, and analyzing additive and multipli-

cative effects across settings. This includes attention to the proximal and

distal settings noted earlier. Of course, the more distal the environment, the

more difficult it is to describe, and the harder it is to trace the processes and

mechanisms through which it affects specific developmental outcomes. Yet

even the measurement of seemingly simple personal characteristics on which

investigators have long relied can quickly become complicated. Indeed, the

measurement of race and ethnicity, socioeconomic status, sex and gender,

and age and cohort are all highly controversial practices in the social sci-

ences (see Oakes & Rossi, 2003; Passel, 2001; Settersten, 1999).

Agency

Matters of agency are as complicated as matters of structure. While the

‘‘structure–agency’’ debate has long been central to the discipline of soci-

ology, the ‘‘structure’’ side has been emphasized more than the ‘‘agency’’

side – except perhaps at the boundary between sociological social psychol-

ogy and psychological social psychology. In psychology, in contrast, the

bundle of concepts associated with the notion of agency is recognized as one

of its central contributions to the study of lives. Despite this, there is little

agreement about what exactly agency is, how it matters theoretically, or how

it should be measured. A wide range of concepts – such as ‘‘self-efficacy,’’

‘‘self-determination,’’ ‘‘locus of control,’’ ‘‘effort,’’ ‘‘mindfulness,’’ ‘‘re-

sourcefulness,’’ ‘‘mastery,’’ and ‘‘autonomy’’ – is often used to index, or

used interchangeably with, ‘‘agency.’’

New depictions of individuals as active and self-aware are especially the

result of a growing ‘‘constructivist’’ view of human development, in which

individuals are seen as the primary architects of their own lives – making

their own decisions, creating their own opportunities, and generating their

own meanings. This reflects a growing recognition of the need to integrate


action perspectives in the field of human development, a trend which is also

mirrored in the discipline of sociology (Giele, 2002). These views, for ex-

ample, have transformed old views of children and childhood, in which the

self was treated as something given, and have instead promoted new views

of the self as something created and reflexive (e.g., James, Jenks, & Prout,

1998).

Constructivist views are equally important for understanding adulthood.

Activities of self-regulation and intentional self-development typically be-

come more differentiated and concrete in the transition to adulthood, as

independence and autonomy gain in importance and as personal goals and

‘‘identity projects’’ are expected to become clearer (Brandtstadter, 1998).

Personal goals and identity projects also undergo a process of continual

revision and readjustment throughout adult life, including advanced old age

(e.g., Freund & Smith, 1999). They are not only shaped by the input of

important others, but are dependent on the plans of other intimates (e.g.,

spouse or partner, children) with whom one’s life is interwoven (that is, lives

must be jointly negotiated and coordinated). They are also shaped by a

larger system of cultural norms, as representations of the ‘‘normal’’ or ‘‘ex-

pectable’’ life course are incorporated into and presumably guide personal

plans. Cultural scripts for life in modern societies seem unclear, and whether

these scripts actually affect personal plans seems even less certain (Settersten,

2003). The pervasive focus on personal control and agency in developmental

science corresponds with the growing emphasis on internal motivation,

planning, decision-making, and open and flexible pathways in societies

(Diewald, 2001).

An important agency-based concept in sociology has been Clausen’s

(1993) notion of ‘‘planful competence.’’ Clausen envisioned the life course as

largely the result of personal choice, and positive trajectories as largely the

result of planful competence. Clausen argued that planful competence is

characterized by three things: dependability, intellectual involvement, and

self-confidence. Based on Q-sort methods, Clausen’s measurement of these

components has been the source of some debate. But as a concept, planful

competence is simple and intuitive: It means knowing your strengths, lim-

itations, and interests, and knowing what options are available and how to

take advantage of them. It means being able to assess the actions and feel-

ings of others, and to take these into account when interacting with others.

Most importantly, it means having goals and the self-confidence to carry

them out, coupled with a high degree of flexibility and openness to new

experiences.


Clausen’s work is important theoretically because it yields interesting in-

sights, especially when combined with recent theorizing on cumulative ad-

vantage and disadvantage over the life course (e.g., Dannefer, 2003).

Clausen suggested that planful competence can be discerned by the ado-

lescent years (but that it develops naturally as adults mature), and that it has

powerful effects on subsequent life. Adolescents who are planfully compe-

tent make good decisions and have successes early in life, the benefits of

which cumulate and lead to further good decisions and successes over the

life course.

If planful competence is a skill that is at least partially learned, interesting

interventions might be designed to help instill it in children and young

adults. This also raises the question of whether it is ever too late to acquire

planful competence, for even individuals in midlife or old age might have

much to gain from it, despite their limited time horizons. Of course, even the

most planfully competent individuals may not be able to act on opportu-

nities if they do not have the social resources to express these competencies

or if other social barriers prevent them from doing so. This is a reminder

that forms of agency are constrained by both barriers in the social world and

personal resources and capacities. Models of agency are, in part, models of

rationality. They take humans to be capable of computing probabilities and

joint distributions, and to have substantial knowledge of their environ-

ments. But these models must be ‘‘faithful to the actual cognitive capacities

of human beings’’ – to real limits in ‘‘knowledge, attention, memory, and

other resources’’ (Gigerenzer, 2003, p. 425).

Attention to agency is important because the life course is to some extent

a personal construction. But the life course also entails selective social

processes that sift and sort people into and out of various settings, and open

or close opportunities depending on the characteristics of people and the

contexts that surround them. These settings and opportunities are not en-

tirely their own doing but are systematically allocated by social forces re-

lated to race, sex, age, socio-economic status, and other factors. It is for this

reason that joint models of agency and structure, to which we now turn,

represent such important ventures for future scholarship.

Agency within Structure

Advances in life course research require greater attention to the joint impact

of social structure and human agency. Models of agency within structure

consider how individuals actively create their own lives and maximize their


own development within parameters set by their social worlds (some of

which may constrain them, and some of which may enable them), and how

individuals interact with and make proactive attempts to alter those worlds.

These models require a stronger partnership between life-span psychology

and lifecourse sociology (see Diewald, 2001; Mayer, 2003; Settersten,

2005a). Sociologists, for example, have often lost sight of the person and

overlooked the roles of personality traits and characteristics, motivations,

and action as life course determinants. From the vantage point of sociology,

however, psychological factors are often viewed as being outside its realm,

given that the purpose of sociology is often understood to be the explana-

tion of the social by the social, a tradition dating back to Durkheim (1895/

1964). Or, if psychological factors are viewed as relevant, they are not taken

to have independent causal significance if they are partially created through

social processes.

Psychologists, by contrast, have often neglected the powerful social and

historical forces that promote or limit development. Given the emphasis on

matters of agency in psychology, a working assumption would seem to be

that individuals are capable of altering structures, or at least are reciprocally

affected by them. Yet modern psychology has focused on family and

interpersonal relationships to the exclusion of more distal settings. In

addition, perceptions, beliefs, and goals are often viewed as separate and

independent from structural opportunities and constraints, which they are

not, for social forces may already be present in psychological states, as noted

above.

The life course is the result of social institutions, culture, and history and

the result of decision-making, action, control, and personality (Diewald,

2001). New models of the life course must capture a more complete range of

these factors. The latter factors, however, traditionally the domain of psy-

chology, will become increasingly important as the strength of institutions

and norms weakens, and as individuals have greater latitude to select or

develop their own life scripts. This freedom results in both new possibilities

and risks, as the experimental nature of ‘‘do-it-yourself’’ biographies makes

them more fragile (Beck, 2000). When individuals choose or find themselves

on pathways that are not widely shared by others or reinforced in institu-

tions or policies, they may lose important sources of support and struggle

with institutional barriers built on more dominant models.

Research in life course sociology and life-span psychology is more com-

patible than it seems at the first sight. Indeed, the more fine-grained work of

life-span psychology, which seeks to unearth the quality and quantity of

‘‘intra-individual plasticity,’’ can offer an empirical base to reinforce


sociological research by decomposing macro patterns into their more prox-

imal and micro-genetic causes (Settersten, 2005a). Together, the two fields

offer tremendous potentials for moving bodies of knowledge from one dis-

cipline to another, and for providing mutually supportive explanations from

different levels of aggregation and with different views on causality. The

concepts of structure and agency, and the dynamics between them, can be

more actively combined in the design and execution of collaborative inter-

disciplinary work (see also Sibeon, 1999).

Several models offer excellent starting points for improving knowledge of

agency–structure dynamics, especially if they are developed in more contex-

tual ways. For example, the ‘‘developmental regulation’’ models of Heck-

hausen (2003) give extensive attention to the selective and compensatory

control strategies of individuals, but relatively limited attention, at least em-

pirically, to larger socio-structural forces that constrain or enable develop-

mental potential. Great possibilities also exist in extending the models of

‘‘selective optimization and compensation’’ of Baltes and colleagues (for ap-

plications to intellectual functioning, see Baltes, Staudinger, & Lindenberger,

1999), and those of ‘‘assimilative’’ and ‘‘accommodative’’ coping developed

by Brandtstadter (e.g., Brandtstadter & Rothermund, 2003), to better incor-

porate socio-structural forces.

Modes of agency within structure cannot be general, but must be under-

stood within particular domains of functioning and particular environ-

ments. They must also consider agency not only as an individual

phenomenon, but as a collective one – as when many individuals who dare

to make innovative life decisions end up creating new options for others, or

when entire groups of people acting in concert attempt to forge new patterns

through social change and collective movements. These models must also

explore how the nature and balance of agency and structure may change

within and between individuals as they move through different periods of

life. This includes a need to assess individuals’ subjective understandings of

their own capacities and resources, as well as those that exist in the settings

around them, for these understandings affect how life is interpreted and

projected forward. It is important to note, however, that life periods them-

selves are important elements of social structure. They are a constant part of

societies (though their boundaries and content may change); they are widely

recognized and have shared meanings; they are reflected and reinforced in

law, policies, and institutions; and they are given practical forms in everyday

life. We now briefly explore concerns related to agency and structure in

three periods: childhood and adolescence; early adulthood through midlife;

and old age.



SPECIFIC LIFE PERIODS

Childhood and Adolescence

Four immediate social settings have been central to understanding the de-

velopment of children and adolescents: families, schools, neighborhoods,

and peer groups and friends (for a review, see Cook, Herman, Phillips, &

Settersten, 2002). This literature also contains debates about the relative

importance of these four settings on child development, the changing

strength and nature of their influence as children become adolescents (es-

pecially what the growing influence of peers and friends, and the emergence

of romantic relationships and sexual awakening, mean for the other set-

tings), and their additive and multiplicative effects. Given the significance of

these four settings and the natural links between them, research on this

period of life has examined person-context interactions more than any other

period. Family characteristics and processes are probably best articulated,

followed by schools, neighborhoods, and peer groups.

Much of the focus has been on the negative forms of these settings – the

problems that ‘‘bad’’ neighborhoods, schools, peer groups, or families pose

for development, and how these settings may be improved to promote pos-

itive outcomes (or at least minimize negative ones). The interesting question

becomes how and why some kids in bad environments manage to do well,

despite these disadvantages, while many others do not (e.g., Furstenberg,

Cook, Eccles, Elder, & Sameroff, 1999). There has been much less attention

to the equally interesting flip-side of these dynamics: how and why some

kids in good environments do not do well, despite their advantages.

Given the emphasis on settings of poor quality, views of ‘‘structure’’ often

carry negative connotations, while those of ‘‘agency’’ carry positive ones.

Here, the agency of children and adolescents, often in combination with that

of adults, is seen as being critical to overcoming the challenges of these

environments – and the lack of agency, in contrast, is seen as devastating.

For example, it is popular belief that success in school, ensured through

hard work and effort, is the key to a better life. In reality, of course, success

in school is more complicated than this. Hard work and effort alone are

often not enough to guarantee success, especially in poor schools and if

coupled with other settings of poor quality. Nevertheless, a core component

of agency in any of these settings relates to how well children are able to

activate whatever resources exist in their environments. This includes build-

ing positive relationships with adults in those settings who have the ability to


take action on behalf the child, or who can protect or promote a child who

might be at risk in some way.

The renegotiation of family relationships as children become adolescents

and young adults is also an important aspect of agency during this period.

Indeed, typical ‘‘acting out’’ in the family or other settings may even be

considered an exertion of agency against structure. Many adolescents also

begin to develop attachments to the world of work through part-

employment and volunteer activities, and become politically and civically

aware. These, too, offer important opportunities to actively develop and

express one’s self.

Of course, agency can also be self-destructive. For example, when parent-

ing strategies are too permissive, when neighborhoods have too little social

cohesion, or when schools are disorganized, children may have too much

autonomy – which can lead to negative outcomes. There is therefore a tricky

balance between structure and agency in promoting positive outcomes for

children and adolescents. The literature suggests the strong influence of

structural forces during this period, expressed through these four settings,

which are created and controlled by adults.

The presence and power of these settings takes place against, and must be

responsive to, the child’s growing need for independence and autonomy.

Much literature, especially in North America, leaves one with the impres-

sion that children and adolescents, as dependents, are not yet capable of

making their own decisions. The emerging field of childhood studies in

Europe is altering this view. This field has been heavily influenced by the

United Nations (1989) Convention on the Rights of the Child. It takes

seriously the notion that children are competent social actors who can – and

should be granted opportunities to – participate in decisions that affect

them, define the directions and processes of their own development, and

participate in the social world (e.g., James, Jenks, & Prout, 1998). This field

has produced innovative studies of how children inhabit and negotiate social

settings, including a range of both public and private spaces with varying

levels of adult control and supervision, and with varying levels of direct

involvement of children in designing and creating those spaces (see, for

example, Philo, 2000).

Early Adulthood through Midlife

Dramatic changes in transitions to adulthood have given rise to a host of

questions about whether current generations of young people are more


dependent on parents, less interested in growing up, and more wary of

making commitments and taking action for themselves and with others. As

movement into adulthood has become more prolonged and complex, par-

ents are now providing extensive support to children well into their 30 s

(Settersten, Furstenberg, & Rumbaut, 2005). The types and levels of in-

vestments needed to launch even well-positioned young people only exac-

erbate the needs of vulnerable populations who have little or no family

support to back them (Foster & Gifford, 2005; Schoeni & Ross, 2005).

Despite the often extraordinary actions required of families to see their

children into adulthood today, young people nonetheless express an under-

standing that they are responsible for their life directions, and that ulti-

mately their own determination and efforts are necessary to achieve their

goals (Furstenberg, Kennedy, McLoyd, Rumbaut, & Settersten, 2004).

While young people today may reach legal adulthood at the age of 18 or 21,

at least in most Western societies, they are often not adults psychologically,

socially, or economically, until much later. A brand new challenge to un-

derstanding this period is how individuals develop a sense of autonomy

amidst increasingly long periods of dependence on others, without strong or

clear scripts to guide them, and when the institutions are based on models of

early adulthood that no longer reflect the realities of the modern world.

Modes of political expression also emerge as concerns during the early

adult years, especially as legal rights of adulthood and responsibilities of

citizenship are granted (Settersten, 2005b). There is convergent evidence that

young adults who wrestle with social issues and participate in civic matters

are more likely to be engaged citizens throughout life (Flanagan, 2004).

With these shifts come new experiences with collective agency, especially

through youth movements, which often coalesce around challenging the

status quo and the power to ‘‘make history.’’ Unlike children, adults can

more easily mobilize and advocate on their own behalf. In the most extreme

of social movements – revolutions – ‘‘structure’’ is something not to be

reshaped but to be toppled (e.g., Blechler, 2000).

The growing autonomy of youth begins an emphasis on both the ability

and need to set developmental goals, harness one’s resources, and exert

control over the environment. Attention to these processes have propelled

agency-related matters to center stage in literature on adult development, as

they are necessary for the successful performance and management of mul-

tiple adult roles. With longer and more certain lives, experiences are po-

tentially more predictable and controllable, and careful planning may be

increasingly necessary in a world with seemingly unlimited opportunities

and time. As suggested in the previous section, adults not only strive for


agency in their own lives, but they also hold positions and take actions that

directly affect the agency of others, especially children. Recent research,

however, has stressed the significant need to understand the ecology of

adulthood – how adults shape and are themselves shaped by the social

spaces in which they exist (for illustrations, see Gecas, 2003; Settersten &

Owens, 2002). New theories and research in these directions are necessary to

compensate for the heavy emphasis on individual capacities and resources in

research on adult development and aging.

In midlife, individuals must confront many new challenges, including

changes or stressors related to physical health, memory, personality, emo-

tional development, adaptation and resilience, work and retirement, and

family relationships (for overviews, see Lachman, 2001). These challenges,

like those of old age, bring new concerns about how to maintain forms

or levels of agency in the face of growing personal constraints, whether real

or anticipated. Yet some of this literature has also emphasized the new

potentials of midlife and the chance to reclaim aspects of the self that were

lost or put aside in early adulthood or to develop the self in whole new ways.

Old Age

Like earlier periods of adulthood, scholarship on old age continues to em-

phasize agency through planning, goal-setting, decision-making, even

through the end of life. What is unique to old age is that these matters

are now assumed to be heavily conditioned by losses in physical, cognitive,

psychological, and social capacities, and by increased dependence on others.

The changing configuration of capacities in old age constrains the degree to

which old people have or can express agency. Indeed, maintaining control

over one’s life is understood to be a (if not the) central task of old age, and

fears about losing control preoccupy many old people. Many of the dom-

inant models in life-span psychology noted earlier (e.g., Baltes, Heckhausen,

Brandtstadter, and their colleagues) have guided this literature. In the light

of reduced capacities and a limited time horizon, opportunities for action –

and those selectively chosen – become especially meaningful to old people

(see also Lang & Carstensen, 2002).

The strong emphasis on individual capacities and characteristics in old

age has resulted in a surprisingly acontextual view of this period – what

Hagestad and Dannefer (2001) call the ‘‘microfication’’ of gerontology –

with the exceptions of the vast bodies of literature on health care environ-

ments, transitions to assisted living or nursing homes, giving and receiving


care, and old age policies (for overviews, see Binstock & George, 2001). In

these cases, agency is a significant concern as old people interface with the

family, institutions, and the state, and as they jointly walk the blurry and

shifting line between dependence and independence. End-of-life issues also

result in a widespread interest in spirituality and religion in old age. In some

cases, this may signal the relinquishment of one’s own control over life and

the placement of control in another entity, or it may signal a shift away from

the self and toward generative actions and ideas meant to improve the lives

of others or humanity.

Gerontology has in the last few decades seriously challenged the belief

that old age is a bleak dark period of great losses. Apart from the areas

noted above, gerontologists seem committed to promoting positive images

of old people and combating negative stereotypes about aging and old age,

especially in promoting models of ‘‘successful’’ aging (e.g., Rowe & Kahn,

1998). These models have helped produce portraits of active elders enrolled

in school, involved in neighborhoods, productive at work, engaged in pol-

itics, and enjoying leisure. All of these spheres offer important opportunities

for expressing agency, especially for those who have health and wealth. The

lengthening period of old age, like that of early adulthood, should prompt

interest in how old people forge pathways through the final decades of life

without strong or clear scripts to guide them, how they navigate institutions

that have no or outdated scaffolding to support them, and how they manage

to retain a sense of autonomy against potentially long periods of dependence

on others.

We must also ask whether we do ourselves and old people a disservice

when we downplay the very real hardships encountered in old age. There is

no document parallel to the U.N. Convention on the Rights of the Child,

mentioned earlier, that enforces special rights for old people. But like chil-

dren, many old people around the world do have special concerns related to

independence, participation, care, self-fulfillment, and dignity, all of which

were recently outlined in the United Nations (1999) Principles for Older

Persons as part of the International Year of Older Persons.

Finally, there is growing empirical support for the notion that the ‘‘ar-

chitecture’’ of the life span becomes increasingly ‘‘incomplete,’’ and that the

relative influence of biology and culture changes, over the course of

adulthood, with cultural influences diminishing and biological influences

increasing over time (Baltes, 1997; Li, 2003). The intersections between

socio-cultural and bio-genetic forces in different life periods offer many

possibilities for exploring structure–agency dynamics, especially during

childhood and old age (see Settersten, 2005c). In this section, we have briefly


considered some of the ways in which the nature and relative balance of

agency and structure may differ across particular life periods. We now turn

to a few observations on the life course as a whole.


THE LARGER LIFE COURSE

There is mounting and conflicting evidence that the life course has become

both more standardized (with regularity in life course patterns being driven

by the increased ‘‘institutionalization’’ brought about by norms, laws, and

social policies) and de-standardized (or ‘‘individualized,’’ with variability in

life course experiences being driven by the greater choices and control in-

dividuals have over their lives). Much contemporary life course research

emphasizes the latter, with widespread belief that a wide range of macro-

and micro-level factors in the last few decades have resulted in life courses

that are less conventional, patterned, and predictable, and more risky in

private and public spheres alike (Mayer, 2004). This seems especially true of

American research, but it is also increasingly true of Western European

research.

Forms of individual agency are assumed to be central to the emergence of

pluralistic life courses. Extending Prout (2005), this diversity is ‘‘locally

constructed’’ through repeated interactions between the self and others in

immediate environments. The fragility of everyday life in the modern world

demands that individuals focus constantly on maintaining and repairing

themselves and social relationships. Only rarely do individuals realize that

the contour of their lives and nature of their experiences may be shared by

many others, wrapped up in larger patterns produced by resources and

constraints of settings well beyond their immediate environments – what

Mills (1959) called the ‘‘sociological imagination.’’ Even individual agency,

as the centerpiece of these models, is often glossed over and taken to be an

essential characteristic that requires no explanation.

The standardization thesis, which suggests the opposite, rests especially

on the strength of welfare states and social policies to regulate particular

transitions (e.g., marriage) or the structure and content of life periods. This

thesis has mostly been based on Western European research, though inter-

national findings are highly variable and heavily conditioned by the type of

welfare-state ‘‘regime’’ and its benefits (for an overview, see Esping-

Andersen, 2002; Mayer, 2001). For example, at one extreme are ‘‘Liberal


Market States,’’ such as the United States or United Kingdom, which pro-

vide only temporary and limited support under specific circumstances. At

the other extreme are ‘‘Scandinavian Social Democratic Welfare States,’’

which provide high degrees of social protection and support across life. In

between are ‘‘Continental Conservative Welfare States’’ such as Germany,

and ‘‘Southern European Welfare States’’ such as Italy.

These views pay special attention to the significance of the state, as a

distal force, in determining the structure and content of the life course.

Again extending Prout (2005), these views are short-sighted because they

take nations and welfare-state regimes to be ‘‘stable and bounded entities.’’

Descriptions of welfare state regimes are important in that these regimes are

frames for understanding how the life course is organized in particular so-

cieties, and are helpful for explaining cross-national variability in life course

patterns. But they reveal little about the dynamic nature of boundaries

within and between societies, or of exchanges across these boundaries. More

importantly, these regimes ‘‘homogenize’’ forms of the life course within

societies because they describe life course patterns more than they explain

how the patterns are produced or maintained. They assume that large-scale

patterns ‘‘trickle down’’ and explain the action of individual and collective

agents, or the options from among which they must choose, rather than seek

to understand how individual and collective activities ‘‘percolate up’’ to

explain large-scale patterns. Evidence of standardization or institutionali-

zation should also not be interpreted to mean that the decisions and actions

of individuals do not matter. Indeed, one could argue that whatever deci-

sions and actions individuals are able to make or take will become even

more precious under conditions of standardization or institutionalization.

The evidence for the two theses also differs depending on whether the

historical view is narrow or wide, and on the phenomena of interest. For

example, while the timing of many American life course transitions became

more uniform over the course of the twentieth century, especially mid-

century, their sequencing simultaneously became more diverse. This is es-

pecially true of transitions typically associated with entry into adulthood

(Shanahan, 2000). Yet for American women – and contrary to contemporary

discussions of the emergence of ‘‘non-traditional’’ family patterns – the

timing and sequencing of family transitions has been high, since at least the

early decades of the twentieth century (Wu & Li, 2005).

This also serves as a reminder that views of institutionalization must span

and be differentiated across multiple domains. Kruger and Levy’s (2001)

distinctions between three types of ‘‘institutional framing’’ will yield fresh

insights into points of positive and negative synergy between institutions,


and into the ‘‘not so visible nexus’’ between men and women. Sequential

institutionalization structures particular life periods and prompts movement

from one period to another. The traditional, but dissolving, lock-step

organization of men’s lives – with education early in life, continuous em-

ployment in the middle, and retirement at the end – symbolizes this type of

institutionalization. Simultaneous institutionalization refers to the attach-

ment of individuals to multiple organizational forms within particular pe-

riods. Popular discussions of the difficulties of balancing work and family

demands speak to the complexities of simultaneous institutionalization

during the early and middle adult years. Adjacent institutionalization relates

to the constraints that other institutions bring for managing work and

family life, such as those posed by schools, public administration, trans-

portation services, or businesses. Kruger and Levy also remind us that much

institutionalization of the life course is not intended or direct, but rather

unintended and secondary. These dimensions of institutionalization also

warrant greater attention in life course research.

The tension between standardization and de-standardization need not be

resolved, with one thesis winning out over the other, as much as actively

seized to more creatively theorize the life course. Indeed, the evidence for

each thesis need not be incompatible and can be simultaneously true, de-

pending on the level of analysis and the target domain or phenomenon

under study. Joint attention to these matters – to the tensions between

structure and agency, to evidence for standardization or de-standardization,

and to the possible connections between them – will advance interdiscipli-

nary research. The most obvious connections to be explored are how (a)

structural factors produce standardization and uniformity (at least for sub-

groups exposed to common forces), and (b) forms of agency produce

de-standardization and variability. But consideration should also be given

to the ‘‘off-diagonals’’ – to how (c) forms of agency might produce stand-

ardization and uniformity (such as when cohort differences in attitudes and

values prompt new and widespread decisions about marriage or parenting),

and (d) forms of structure might produce de-standardization and variability

(such as when disorganization in, or poorly coordinated connections be-

tween, educational institutions and the labor market results in disjointed or

incoherent experiences).

Models of agency within structure, described earlier, are central to un-

derstanding how the life course is partly the result of active and free choices,

partly created within a fixed set of possibilities and partly imposed from

outside – all of which come with consequences, some good and some bad,

for individuals, depending on how far their paths stray from others or


deviate from those assumed in social institutions and policies. These models

will demand clearer definitions and more precise measurement of ‘‘agency’’

and ‘‘structure,’’ their characteristics and processes, and their sources and

determinants.

A central challenge here is that the lives of individuals and successive

cohorts have changed rapidly, but the assumptions that underlie social

institutions and policies are often based on outdated models of life (see also

Settersten, 2005b). These mismatches may bring serious risks for the func-

tioning of individuals and societies, and there is significant need to re-

architect social institutions and policies so that they better meet the changing

needs and realities of individuals and societies. New commitments must si-

multaneously improve and make more flexible the institutions through which

individuals move (including endorsing or permitting a wider range of paths),

as well as improve and make more flexible the connections between them.

New commitments must also strengthen the skills and resources of individ-

uals so that they can better navigate the life course, for these capacities are

vital to ensuring positive outcomes amidst the rapid social dramatic change

and great uncertainty of the contemporary world.

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56

AGENCY, EVENTS, AND

STRUCTURE AT THE END OF THE

LIFE COURSE$

Victor W. Marshall

My purpose is to address a number of related conceptual issues that deal

with the basic, conceptual building blocks of the life course perspective.

These building blocks have been described, over time and from different

perspectives, in different languages. There is the language of the psychology

of human development, with its stages, crises, and developmental transfor-

mations. There are different languages within sociology, describing careers,

status passages, events, transitions, trajectories, and so forth. Many of these

terms appear to be synonyms, different words for the same underlying con-

cept. There is also at least one case in life course scholarship where one word

is used with many different underlying concepts: that is the word ‘agency’,

$This paper draws in part on an earlier paper, ‘‘Agency, Structure, and the Life Course in the

Era of Reflexive Modernization’’ presented at the American Sociological Association meetings,

Washington, DC, August 2000. I have had the pleasure to discuss many of these ideas with my

colleague, Glen Elder at UNC; with Margaret Mueller, our former student at UNC; with two of

my former students and continuing colleagues, Philippa Clarke (now at Duke University) and

Julie McMullin (now at The University of Western Ontario). I am indebted to them for their

insightfulness, intellectual honesty and collegial support. I also thank the students in my grad-

uate seminar in current issues in sociological theory, Department of Sociology, University of

North Carolina, with whom many of these ideas were discussed.





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10002-1

57

which takes on many meanings in different disciplines, but also within the

same discipline and indeed within the writings of the same author.

I will address three questions with reference to my own early research on

the sociology and social psychology of aging and dying. Thus, the paper re-

interprets work that I did, for the most part prior to the emergence and

formalization of the life course paradigm, in order to address conceptual

and methodological issues of today in life course terminology. The first

question that I shall address is conceptual that asks whether there exists, in a

given society, values for human development and the life course, that is, a

shared view of what is a good life course. I will examine this with reference

to the concept of the ‘good death’. The second, methodological, question

asks how the different concepts (such as trajectory, stage, transition, and

event) are usually operationalized, and with what consequences. I will ex-

amine this with respect to the comparison of ‘objective’ with ‘subjective’

data about the dying trajectory. The third, results, question asks to what

extent changes in status result in the redefinition of identity. I will address

this question by referring to the distinction between self and identity, and to

the action of the aging and dying individual, in managing identity.

I am thus revisiting my early scholarship undertaken before the life course

perspective came to be institutionalized in the social sciences. I am person-

ally curious to see how the life course perspective can shed new light on my

scholarship of 30 years ago; and I hope that this exercise will help to clarify

some of the concepts of the life course that are currently in vogue.

Older individuals face objective changes in their life situation and their

sense of themselves in time. Events and the entire past and anticipated life

course have taken on a new meaning in an interplay between what is in-

creasingly referred to as agency and social structure. I find that terminology

problematic and, before turning to examine these three research questions in

light of my data on aging and dying, I will deal at some length with the

concept of agency and the agency–social structure linkage in sociology.

1. AGENCY

Every social theory, whether implicitly or explicitly, has to deal with two

things – energy and direction. Without energy there would be no action. One

metaphor for this is heating a house. You need to have a furnace to produce

heat. But to understand the heating of a house you also have to account for

direction – how and where that warm air moves from the furnace, through

ducts, to the various levels and rooms of the house (this metaphor rests on a

VICTOR W. MARSHALL58

more North American than European concept of how heating systems

work). Turning to social theory, the two easiest examples would be

psychoanalytic thought and symbolic interactionism. For Freud and his

followers, the id is the furnace, it is the source of energy. But that energy

needs channeling, and the complexities of psychoanalysis provide an expla-

nation for how this energy is channeled into socially acceptable behavior (or

not). In symbolic interactionism, the ‘I’, acting in the dialectic of the self

process, is a necessary hypothetical construct if the perspective is to move

beyond stasis to process. One variety of symbolic interactionism, the Iowa

School, downplays the ‘I’ to focus on the ‘me’ (which is akin to the duct

work in the furnace metaphor in steering behavior). In emphasizing the me,

it loses the sense of process or dynamism that is such an important con-

tribution of the symbolic interactionist perspective.

Agency lies somewhere in the area of energy. The term has increasingly

popped up in the sociology of the life course, but rarely is it defined or

explored in great detail. Even more rarely is agency measured. Is agency just

a new name for something old, such as the concept of action or the concept

of voluntarism? The term agency is used in conflicting ways, but always has

something to do with choice. Dannefer and Uhlenberg decry the way choice

is handled in life course research and theorizing:

In the study of action, choice is a problem to be analyzed, not an accomplishment to be

asserted (Dannefer, 1999). Given the problematic epistemological and ontological status

of ‘‘choice’’ in the wider social science literatureyits remarkably unproblematic ap-

pearance in life course theory cannot be defended. What is almost always measured in

such discussions is behavior, and it is simply presumed that behavior is based on choice

(Dannefer & Uhlenberg, 1999, p. 312).

The fuzzy and conflicting treatment of choice and agency has also been

recognized as a major problem for more general sociological theory. Thus,

Meyer and Jepperson (2000, p. 101) charge that ‘‘ythere is more abstract

metatheory about ‘actors’ and their ‘agency’ than substantive arguments

about the topic.’’ It is a concept more often invoked than measured.

1.1. Agency as a Principle of the Life Course

Glen Elder has been more specific than anyone else in the aging and life

course domain to define this concept. For Elder, agency is one of five

defining principles of the life course. Here is the principle: ‘‘Individuals con-

struct their own life course through the choices and actions they take within

the opportunities and constraints of history and social circumstances’’

Agency, Events, and Structure at the End of the Life Course 59

(Elder & Johnson, 2003). Settersten (2003, p. 39) lists the same as ‘‘an

‘emerging proposition’ of the life course perspective: The life course is a

partial product of human action. It is actively created within the confines of

the social worlds in which individuals exist. Life course scholarship therefore

promotes models of human agency within structure.’’ But this principle has a

long history in developmental psychology and life course sociology. I would

therefore like to review a bit of that history.

1.2. Agency as Production of a Life

Of many arbitrary starting points,1 I will begin with a largely psychological

publication in the human development area. In the Foreword to Lerner and

Busch-Rossnagel’s (1981a) important edited volume, Individuals as Produc-

ers of their Development: A Life-Span Perspective, sociologist Orville Brim

notes that ‘‘The idea that organisms act to create environments to elicit

responses from themselves is not new.’’ However, Brim argues that the 1981

volume is the first to treat the idea broadly and in the context of the theory

of life-span development. While the term, ‘agency’, does not seem to be used

in the volume, the idea of agency is there. As Brim (1981, pp. xv–xvi) puts it:

Behind this idea, to be sure, is the view that the organism is dynamic, powered by

curiosity, growth, expansion, and a drive toward mastery over itself and its world; and

also by the development during the first two years of life of a sense of self as a distinctive

being, and the construction of images of future selves that are different from what one is

now. Behind the idea is also the view that organisms are open to change, are much more

malleable than heretofore thought, and that the consequences of early experience and

biological endowment are transformed by later experience.

The Lerner and Busch-Rossnagel volume lays a broad foundation, from

psychology, evolutionary biology, and anthropology, for a view that indi-

viduals are not only produced by, but also produce their world. This work

suggests that the capacity for organisms to produce their own world varies

both ontogenetically and phylogenetically. In terms of individual human

lives and ontogenetic development, the capacity to act on the world and to

have greater ‘plasticity’ of function increases with human development, and

this capacity can be described in terms of physiological changes in the brain

and, more broadly, the entire organism (Lerner & Busch-Rossnagel,

1981b).2 I will turn to the sociological foundations of this same notion

later, but in the life course perspective, which is explicitly interdisciplinary,

this is an important line of theoretical reasoning related to the concept of

agency, and one that continues to the present (e.g., Diehl, 1999).


Without using the term agency, in his Foreword to the volume Brim raises

the possibility that agency might be considered a variable as contrasted with

simply an aspect of human nature – the ability to choose: ‘‘Throughout this

volume the subject is humanity and the concern is with the species rather

than with individual differences. Certainly there may be individual differ-

ences in degree of mastery of, and re-creation of, the environment because of

individually endowed or acquired differences, but the idea refers to the main

thrust of the human animal, not an occasional remarkable human being’’

(Brim, 1981, p. xvi).

1.3. Agency as Environmental Proactivity or Adaptation

Another line of research in psychology, at times related to the exploration of

Erikson’s developmental framework, either explicitly uses the concept of

agency or uses related terms, which are themselves conceptualized in terms

of agency, and which considers agency to be a variable, and measurable,

property of individuals. Lawton (1989), whose earlier work on person–

environment fit and the ‘environmental docility hypothesis’ viewed the

individual as largely reactive to environmental limitations and pressures,

subsequently introduced the hypothesis of ‘environmental proactivity’, to

recognize ‘‘action and agency – the person’s competence as a determinant of

environment’’ (Lawton, 1989, p. 140).

Building on Lawton’s conceptualization, Kahana and Kahana (1996)

used the term agency in a paper promoting the concept of ‘proactive ad-

aptation’. Here is what they say:

Dependency models of aging have emphasized the propensity of older adults to be

passive respondents to environmental influencesy. However, there has been a small but

growing group of gerontological researchers, who have recognized that older persons

can play significant proactive roles and behave in ways that draw upon and can generate

resources in their environment (Lawton, 1989). This orientation parallels theoretical

developments in the broader field of sociology (Giddens, 1983) that increasingly rec-

ognize the role of agency, reflecting progress, intentionality, and responsibility in the

actions of human beings.

1.4. Agency as Masculine Trait

The term agency has been used by psychologists investigating the concept of

‘ego strength’, and studying the relationship between agency and Eriksonian

‘generativity’. Gutmann (1965) noted that the psychological concept of ‘ego


strength’ had decidedly masculine properties. Building on Gutman, Bakan

distinguished between ‘agency’ and ‘communion’, with the former referring

to ‘‘the existence of an organism as an individual’’ and the latter to ‘‘the

participation of an individual in some larger organism of which the indi-

vidual is a part. Agency manifests itself in self-protection, self-assertion, self-

expansiony’’ (Bakan, 1966, p. 15). The life course sociologist Alice Rossi

(1980, p. 9) uses the term ‘affiliation’ instead of ‘communion’, in order to

‘‘avoid any theological association with the concept’’, but retains Bakan’s

concept of agency.

In a more recent paper, psychologists measured agency as both a psy-

chological trait and a characterization of behavior (Ackerman, Zuroff, &

Moskowitz, 2000). Although an independent definition of the construct,

agency, was not given, the conceptualization relates it to ‘masculine’ as

opposed to ‘feminine’ traits and behavior. The self-report measure of agency

is based on the measurement of psychological traits through Likert scales to

assess ‘self-assertive-instrumental traits’ versus ‘interpersonal-expressive

traits’. These generate ‘masculinity’ and ‘femininity’ scores that are con-

sidered to represent agency (masculine) or communion (feminine). An

alternative adjective-rating scale contrasts ‘love’ and ‘status’, the latter

measure a conceptual domain ‘‘from dominance to submission and is

thought to represent agency’’ (Ackerman et al., 2000, p. 30). Behaviorally,

self-described behaviors were coded on four dimensions: dominance and

submission, and agreeableness and quarrelsomeness. Dominance minus

submissiveness is considered to represent agency, while agreeableness minus

quarrelsomeness is considered to represent ‘communion’. Agency, measured

in these ways, is implicated in predicting generativity (as conceptualized by

Erikson), and this relationship, the authors speculate, is because ‘‘creating is

a primarily agentic form of generativity’’, such that ‘‘highly agentic indi-

viduals manifest generative concern focused on creatingy’’ (Ackerman

et al., 2000, p. 37).3 In this psychological formulation, it is clear that agency

is considered to be a variable personality trait of the individual.

The issue of measurement of agency, an issue that is boldly addressed in

the article by Ackerman et al., cannot be dismissed. For example, George

(1999) who is sympathetic to an ‘agentic’ perspective on the self in relation

to society, points out that ‘‘Researchers typically assume that behavior ob-

served in natural settings is triggered by self-perceptions and self-related

motives, but that view remains more assumption or interpretation than

documented fact’’ (p. 47). George is here arguing that the aspect of agency

asserted by many, often qualitative, researchers is nothing but the invoca-

tion of an unmeasured, hypothetical construct. The construct is inferred


from the behavior that it is presumed to cause. If Ackerman’s measurement

approach is problematic (as it seems to me), this may be more in its

execution than its intent.

1.5. Agency as Making Possible ‘Loose Coupling’ (Agency as

‘Unexplained Variance’)

Let us return to Glen Elder. Elder (1985, p. 42) has noted that ‘‘An agentic

concept of individuals in shaping their own trajectory has been a central

principle of the life course framework.’’ Indeed, Elder has made it so. He

notes that conceptions of human agency have characterized ‘life-history’

studies at least since the classic study of the Polish peasant in Europe and

America, by Thomas and Znaniecki (1918), Elder (1997), and Elder and

Johnson (2003). Elder sees agency as important in selection processes:4

‘‘Within the constraints of their world, competent people are planful and

make choices among alternatives that form and can recast their life course’’

(Elder, 1997, pp. 964–965). Agency refers to choices among available

options, and Elder relates this to his concept of ‘loose coupling’: ‘‘Loose

coupling reflects the agency of people even in constrained situations as well

as their accomplishments in rewriting their journeys in the course of aging’’

(Elder, 1997, p. 965). Constraint is accounted for, in Elder’s formulation of

the life course perspective, by ‘‘The principle of historical time and place:

The life course of individuals is embedded in and shaped by the historical

times and places they experience over their lifetime’’ (Elder & Johnson,

2003, p. 62). Elder’s own work has emphasized the impact of the depression

and WW II as structuring choices. Agency can only manifest itself through

choice, and choice is possible only if there are alternatives.

In Elder’s usage, agency makes possible ‘loose coupling’, which in turn is

a ‘principle’ of life course theory that makes room for departures from

structure. In a way, agency functions in this theoretical perspective in the

same way that ‘unexplained variance’ functions in statistical models: if be-

havior is not patterned structurally, then it must reflect resistance to struc-

ture. As Elder and O’Rand (1995) put it, ‘‘Loose coupling reflects the agency

of individuals even in constrained situations as well as their achievements in

rewriting past journeys in the course of aging’’ (p. 456). The relationship of

agency to structure is complex for Elder. Agency is presumably necessary if

individuals are to operate either within or outside the boundaries of social

structure. As Elder and O’Rand (1995, p. 457) argue, ‘‘Age grades and loose

coupling exemplify two sides of the adult life course – its social regulation


and the actor’s behavior within conventional boundaries, and even outside

of them’’.

1.6. Agency as Overcoming Resistance

While Elder seems to argue that humans always exercise agency (from which

we might infer agency is a property of human nature), the way in which

Elder describes agency sometimes leaves the implication that it is manifested

only at critical turning points of the life course, or in resistance to the

established social order. I will first deal with the latter: agency as overcom-

ing resistance. Thus, Elder (1997) argues that loose coupling reflects the

antithesis of age grading, and that the agency involved in loose coupling

exemplifies ‘‘the actor’s initiatives and interpretations that press for indi-

viduality and deviations from convention’’ (p. 965). This would imply that

agency is not exemplified if someone follows convention, complies with

social norms or social control pressures, or leads a life in conformity with

social institutions. This is a much narrower view of agency than, for ex-

ample, Giddens’ arguments. The same view is found in Heinz (1996, p. 57),

who notes that while biography is constructed with guidance from ‘‘insti-

tutional standards and the unequal distribution of resources for building

continuous life paths’’, ‘‘Such limitations must not be seen as fateful con-

straints, they can be overcome by human agency.’’5

In setting agency as against social structure, Elder and others adopt a

specific, and questionable, stance about the link between the individual and

social structures. This point has been vigorously made by Dannefer and

Uhlenberg, and can also be pursued through Giddens, Gubrium and others.

Dannefer (1999) links agency in a fundamental way to the processes of

world construction without which there would be no social system. As he

puts it (p. 73), ‘‘yhuman behavior is purposeful; it is not guided by instincts

but by intentions (Weber, 1978).’’ The interaction of intentional actors co-

produces the social system at both micro and macro levels and the self-hood

or social (human) nature of the actors themselves (Dannefer, 1999).6

1.7. Agency as Evidenced in Life Transitions

Moreover, at times in Elder’s writings, agency is indicated not in the smooth

flow or the routines of everyday life, but in life’s more dramatic moments,

moments of transition rather than of continuity, even if the notion of


resistance is not implied. This theme runs through Elder’s work. Thus, ‘‘No

idea better illustrates the contemporary link between social context and the

agency of the individual than the concept of life transition, which defines the

problem as a change in states – social and psychological. Adults bring a

history of life experiences to each transition, interpret the new circumstances

in terms of this legacy, and work out adaptations that can alter their life

course. When transitions disrupt habitual patterns of behavior, they provide

options for new directions in life, a turning point’’ (Elder & O’Rand, 1995,

p. 456). This notion that agency is found only at transition points or, as

Giddens (1991a, p. 113) calls, them, ‘‘fateful moments,’’7 or the related

notion that it is found only in resistance to social barriers, is at odds with the

notion that agency is a property of human nature. Three different notions of

agency are found, I believe, in Elder’s work (capacity, resistance, transition),

leading to the question of whether different terms should be used to more

clearly indicate these three meanings.

1.8. Agency as Responsibility

As the last in this long list of usages of agency, let us turn to the notion of

‘being an agent’ for someone or something. While this is perhaps the most

commonsensical, lay-usage meaning of agency, it is little found in life course

studies. Rather, it is considered to be a sub-theory of rational choice theory

(Kiser, 1999) that draws on Max Weber’s sociology and the ‘new institu-

tionalism’ in economics. According to Kiser (1999, p. 146), ‘‘An agency

relation is one in which a ‘principal’ delegates authority to an ‘agent’ to

perform some service for the principal’’. This notion can, however, be re-

lated to life course theorizing. Meyer and Jepperson (2000) have recently

provided a detailed theoretical examination of the social construction of

agency, in which they argue that the modern actor is a cultural construction

and is ‘‘yan authorized agent for various interests (including those of the

self)’’ (p. 101). For them, agency is ‘‘legitimated representation of some

legitimated principal, which may be an individual, an actual or potential

organization, a nation-state, or abstract principlesythe concept ‘agency’

draws attention to the devolution of external authority, and to the external

legitimation and chartering of activity’’ (Meyer & Jepperson, 2000, note 2).

I can act as an agent for someone else or someone else can act as my agent.

But I can also be constructed as having authority to act for myself, to be my

own agent, to act on behalf of myself. This may be formulated in terms of

the I–me distinction found in G. H. Mead (as the authors acknowledge) and


symbolic interactionism (p. 111). In life course parlance, Elder and O’Rand

(1995, p. 465) note that ‘‘ypeople function as agents of their own life course

and development’’. In terms of the sociology of the risk society and its

implications for the life course, as in so much European life course theo-

rizing (see Marshall & Mueller, 2003), a move to the risk society may be

characterized by an increasing delegation of authority for life course man-

agement from society to the individual.

Moreover, Meyer and Jepperson (2000) maintain, in the modern world I

am more and more called upon to assume such agency, in large measure

because of a decline in the attribution of agency to spiritual forces: ‘‘Some

agency is built into modern pictures of the agentic authority and respon-

sibility of the state and other organizations; much devolves to the modern

individual, who is empowered with more and more godlike authority and

vision’’ (p. 105). This notion of agency therefore extends to ‘being respon-

sible for your self’, in which case it points to the reflexivity of the self –

taking responsibility for the self as an object.

In summary, I have reviewed many ways in which the concept of agency,

either directly so named or denoted by a closely related term, has enriched

life course and aging studies. In a very general way, agency has been seen as

the production of a life. The agent is the producer; human development, the

lived life, the narrative, is produced by agency. In more specific formulations

along the same lines, agency has been viewed in terms of environmental

proactivity or adaptation. This recognizes that people not only react but act

and, in acting, produce their biographical selves.

Agency has also, in a few studies by psychologists, been seen as a mas-

culine trait – doing rather than being, taking charge, making things happen.

This is clearly not logically consistent with the previously discussed notions,

which can all be grouped as making an assertion about the nature of being

human. Rather, it postulates that agency is a variable property of humans,

albeit one much more often found among men than women.

In the most widely accepted theoretical statements about the life course, by

Elder and those who have adopted his language, agency has been seen as the

force making possible ‘loose coupling’, thereby somehow accounting for the

fact that all people do not follow standardized, institutionalized life courses.

But I question whether this adds anything to an explanation. Is it not the

same as depicting ‘unexplained variance’ in multivariate models? One can

draw a ‘causal arrow’ to show where variance is unexplained, but the arrow

emanates from something unexplained. If you push that kind of argument

too far, you end up with unexplained variance as the ‘uncaused cause’, which

St. Thomas Aquinas argued was proof for the existence of God.


While the above usages of agency are quite general, some uses are highly

specific, referring to agency only if the individual is overcoming or resisting

social structural barriers, or only if the behavior of the individual is with

respect to a major or important life transition. In such formulations, people

would not be ‘agentic’ most of the time, but only at critical points in their

lives.

And, finally, I noted the usage that suggests agency refers to culturally

legitimated responsibility to act – on behalf of others, of organizations or

ideas, or of one’s own self. This is quite a general conceptualization of

agency, but more restricted than the first ones, which would view the ca-

pacity to act as agency, regardless of its cultural or social legitimation. This

usage of the concept of agency also treats it as a variable, and the authors

I drew on specifically trace a historical shift of agency from spiritual forces

to institutions and to individuals.

2. MAKING SENSE OF AGENCY: TOWARD

TERMINOLOGICAL CLARITY

To summarize, I have noted that the term agency has many meanings in the

sociology and the psychology of aging and the life course. I find it generally

useful to use specific terms for specific meanings. It may be necessary to

distinguish between four different sorts of things: (1) the human capacity to

make a choice, that is, to be intentional; (2) the resources within the indi-

vidual or at the command of the individual that can be brought to bear in

intentional or agentic behavior; (3) behavior of individuals that reflects

intention; and (4) the social and physical structuring of choices. As I turn to

elaborate on these, I note that each of these four constructs has a meta-

phorical place in the analogy of the furnace that I developed earlier.

By the first, I mean that a social theorist may propose that a capacity to

exercise choice is a fundamental aspect of human nature. In terms of the

metaphor, this corresponds to the physical fact of the furnace. Without the

furnace there is no capacity to heat the house. This is not to deny that this

capacity develops in the human over time. Such a capacity would include

awareness and thus requires cognitive capacities to give identity to objects

and events in the world. This can be seen as agency in this first sense and it

can be viewed as a developmental capacity of (virtually) all humans.8

I recommend using the term agency in this way to refer to the human

capacity (as aspect of what it is to be human), to act intentionally, planfully,


and reflexively and in a temporal or biographical mode. Except in explicit

developmental work, most life course researchers would not need to treat or

measure this as a variable. Rather, it is an assumption about human nature,

of the order, ‘‘all human beings have free will’’.

By the second construct, the resources that can be brought to bear in

agentic behavior, I refer to both personal capacities of the individual and

resources at his or her command. Metaphorically, this corresponds to the fuel

for the furnace. Some furnaces might have more, or better, fuel than others.

Personal capacities might be intelligence or stage of cognitive development,

learned skills or abilities, knowledge, or physical strength and talent. Re-

sources might be economic (wealth), social (social capital, networks, con-

tractual or informal social ties, and alliances). I do not think it is helpful to

use the term agency to describe this variable property, which is better called

resources or perhaps assets. Resources can be measured, although with care

to include those ‘within’ the individual (e.g., education), and external to the

individual but at his or her ‘command’ (e.g., social capital). Clausen calls

‘‘planful competence’’ the ability to make informed, rational decisions, and

set realistic short- and long-term goals (Clausen, 1991). I would consider

‘planful competence’ to be a resource, rather than a capacity. Clausen (1991,

1993) saw planfulness as indicated by self-confidence, intellectual investment,

and dependability. Shanahan, Elder, and Miech (1997, p. 59), drawing on

Clausen’s concept of planfulness, defined ‘planful competence’ as ‘‘The extent

to which an individual is aware of his or her knowledge, abilities, and in-

terests; pursues goals; and reflects on important decisions’’. They found

‘‘reasonable approximations’’ for these dimensions in the Stanford–Terman

data they used in parents’ assessments of these traits in their children. These

are not basic traits of human nature, but variables in the ability to do the

things that humans do. There is no reason why measurement of these di-

mensions of planful competence, as a resource, could not be further devel-

oped. Presumably, more objective measures would include not only the

awareness of an individual of his or her knowledge, abilities and interests, but

the actual existence of these things regardless of awareness of them. One can

have an ability without knowing it, and this might have resource conse-

quences even if not at the level of awareness.

By the third construct, the actual behavior that reflects intention, I refer

to what has been called ‘action’ or ‘voluntaristic action’ or ‘social action’ by

a host of sociologists who rarely used the term ‘agency’. I think the term

‘action’ is appropriate here, although I have a trained incapacity, as a so-

ciologist, to conceive of action as other than social (see, Campbell, 1996).

This term is itself manifestly used in various contradictory ways, as Colin


Campbell (1996), Derek Layder (1994), and others have shown, but I think

it will be useful if specified in this way. In terms of the furnace metaphor,

this construct would be captured by adjusting or programming the ther-

mostat and opening and closing duct openings and valves.

By the fourth construct, the social and physical structuring of choices,

I refer to the de facto structure of opportunities or life chances that is open

within the range of action of the actor. Metaphorically, the construct

corresponds to the duct work, the degree of insulation, the leaky windows,

and other structural aspects that pose barriers to effective home heating –

regardless of how good the furnace, the fuel, and the thermostat. The con-

cept of ‘life chances’ (Dahrendorf, 1979; Weber, 1978) is useful here, so long

as it is recognized that life chances are not static but emanate from social

processes.

It is useful to emphasize the co-constitution of self and society, through

which action creates social structure just as social structure constrains, or

opens up, possibilities for choice and, thereby, ‘structures’ action. A large

number of life course theorists have enjoined their colleagues to avoid the-

orizing ‘‘agency without structure’’ or ‘‘structure without agency’’ (George,

1999; Ryff, Marshall, & Clarke, 1999; McMullin & Marshall, 1999;

Marshall, 1995, 1996; Settersten, 1999, p. 223). The broader challenge is to

theoretically address the linkages between agency and structure or self and

society (Campbell, 1996; Layder, 1994; Ryff & Marshall, 1999).9 Moreover, if

agency is difficult to define, so too is social structure (Alwin, 1995).10 I turn

next to that conceptual issue.

3. DEFINING SOCIAL STRUCTURE

Despite its centrality to the very core of sociology as a discipline, a plethora

of definitions exists for the term and its conceptualization varies greatly.

I relate a conceptualization of social structure directly to the aforemen-

tioned Weberian concept of life chances, and I draw very heavily on Sewell

for this. If it is assumed that human beings have agency (the capacity for

choice) and that they manifest agency through their actions, then the di-

lemma is to find a way to define social structure in a way that recognizes

agency and action without sacrificing the notion of social constraint. Berger

and Luckmann (1966, p. 18) approach the action-structure problem by

outlining three dialectical processes in which ‘‘subjective meanings become

objective facticities’’. The first of these is ‘externalization’, the process

through which individuals create their social worlds through their action.


The second is ‘objectivation’, through which once created, these social con-

structions take on a reality of their own, independent of their constructors.

The third process, ‘internalization’ refers to the process by which the ‘‘ob-

jectivated social world is retrojected into consciousness in the course of

socialization’’ (Berger & Luckmann, 1966, p. 61). As Smelser (1997a, b) has

noted, this concept does not fully capture the ways in which reality con-

strains action. Rather, objectivated reality ‘acts back’ upon individuals

through a number of ‘reality maintenance’ devices, often making use of

language and ritual, and with institutional and physical traces as well (see

McMullin & Marshall, 1999). It is the latter – ‘institutional and physical

traces’ – that moves the conceptualization somewhat beyond Giddens’

(1991b) concept of the duality of action and structure. There is something

more enduring than the instantiation of reproduced social structure through

action, and this ‘something’ can take a number of forms, including legal

arrangements, rules and regulations, and the capacity to mobilize resourc-

es.11 For example, an army is more than the activities of its personnel. It

includes its authority and reporting structure and, not the least, its person-

nel, weapons, and supplies. It is true, as Giddens (1984) argues, that these

other things come to be through human action over time, but they are ‘real’

and constraining of action all the same, and independent of the action that

constitutes them and certainly more than ‘‘memory traces, the organic basis

of human knowledgeability, and as instantiated in action’’ (p. 377). As

Sewell (1992, p. 3) remarks, ‘‘ythe notion of structure does denominate,

however problematically, something very important about social relations:

the tendency of patterns of relations to be reproduced, even when actors

engaging in the relations are unaware of the patterns or do not desire their

reproduction’’.

While Berger and Luckmann are helpful in emphasizing ‘objectivated’

social reality as one moment in the dialectic, I would emphasize an insight

from one of their intellectual sources, Alfred Schutz (1964), which is that

people are not fully free to fashion their own lives because they are born into

a world of predecessors. Archer (1995) suggests that we have to analytically

stretch out the three modes of a dialectic similar to that of Berger and

Luckmann’s in order to recognize that there are objective (material) con-

ditions that already exist prior to the initiation of action.12 For her the

modes are structure, interaction, and structural elaboration. For Sewell,

ideas of social structure are more than the internalized ‘rules’ of Giddens

(and perhaps Berger & Luckmann), but they are socially shared ideas, which

he prefers to call ‘schemas’, and which I would consider to be cultural; and

for Sewell (1992, p. 13), ‘‘Sets of schemas and resources may properly be


said to constitute structures only when they mutually imply and sustain each

other over time.’’ And in this framework, which I endorse, as Sewell states

it, ‘‘Agencyyis the actor’s capacity to reinterpret and mobilize an array of

resources in terms of cultural schemas other than those that initially con-

stituted the array’’ of resources available to them. Agency is then the human

capacity to make something new, to contribute to innovative social

production of the world.13

4. AGENCY AND STRUCTURE IN THE LAST

CHAPTERS OF LIFE

At this point, I will use these constructs to briefly summarize the findings of

my research on the social psychology of aging and dying, and relate the

discussion to the specific research questions mentioned at the beginning.

4.1. My Early Research in Aging and Dying

I will briefly outline my research in the sociology of aging and dying, which

began with my doctoral dissertation work. Georg Simmel has set out a basis

for my research questions early in the last century

ywe are, from birth on, beings that will die. We are this, of course, in different ways.

The manner in which we conceive this nature of ours and its final effect, and in which we

react to this conception, varies greatly. So does the way in which this element of our

existence is interwoven with its other elements (Simmel, 1950).

I set out, in my dissertation, to find some answers to the questions implied in

Simmel’s statement, to wit:

How is it that people die in different ways? How is it that people conceive their nature

and its ‘final effect’? How do people react to their conception of death? In what way is

the individual’s conception of his mortality interwoven with other elements of his life?

This study focuses on the aging, and attempts to assess the implications of the fact that

the aging are also dying. It is, therefore, not a study of death or dying per se, but rather

of living under the growing realization of impending death (Marshall, 1972, p. 2).

In contemporary life course terms, I was interested in the final trajectory

before the transition to dying, and to investigate this trajectory I would have

to identify the transition that led into this trajectory. We are all dying of

course, but my interest was in that period in which the individual became

more highly aware of this fact, and I used the term ‘awareness of finitude’ to


describe it. I pursued this research in a field study using participant obser-

vation and semi-structured interviews in a middle-class retirement commu-

nity and a nursing home that had a clientele of a much lower social class

background.14

I was not using terms that predominate in current life course usage.

Leonard D. Cain Jr. (1964) had already published the first major statement

about the life course in an important essay, ‘‘Life course and social struc-

ture’’, in which he aimed ‘‘to identify, isolate, and systematize a life course,

or age status, frame of referencey’’, as well as to ‘‘contribute to the ad-

vancement of a sociology of age status’’ (Cain, 1964, p. 273). But I did not

cite it. He had been influenced by Anselm Strauss, and I did draw on

Strauss, and his concept (with Glaser) of status passage, which is a direct

steal from the career concept of Everett Hughes (1971). But it was only in

developing publications from my dissertation that I moved deeply into using

the career and status passage concepts (Marshall, 1978–1979, 1980).

This was before much of the current life course terminology had been re-

introduced by Elder (1975) and Riley (1979). After more than a decade of

‘chewing on’ my data, I developed a metaphor to describe the main line of

my theorizing and published this in a book, Last Chapters: A Sociology of

Aging and Dying (Marshall, 1980). In this metaphor, I saw aging individuals

as if they were about to author the last few chapters of their lives. With the

inevitability of death approaching, I saw them looking at their own bio-

graphies, and motivated to develop a stance that their lives made sense. (See,

McAdams, this volume, for a general approach to narrative and the quest to

make sense of one’s biography. My metaphor is based on similar psycho-

logical assumptions about motivation, but the elements of the metaphor are

specific to the situation of heightening awareness of finitude.) This meant

making sense of the earlier chapters of the autobiography, but also of the

end of the biography in death itself. Moreover, I saw people in such cir-

cumstances as wanting to assume responsibility for their lives as a whole.

These metaphorical elements stood for the theoretical dimensions of recon-

structing and legitimating one’s past biography, developing a legitimation

for the self as dying, and assuming control, or responsibility for the life as

lived and as ending.

4.2. Is there a Shared Conception of what is a Good Life Course?

I turn now to the first question that I shall address. This conceptual question

asks whether there exist in a given society values for human development


and the life course, that is, a shared view of what is a good life course. I will

examine this with reference to the concept of the ‘good death’. At one level,

this is a question of culture; at another, it is a question of the individual.

I drew on the anthropologist, David Counts (1976) for a cultural conception

of ‘the good death’. The Kaliai of Western New Britain hold a cultural

conception of the ‘good death’ that is quite similar to the process of aging

and dying that is held to be ideal in the disengagement theory of aging, a

North American model (Cumming & Henry, 1961). As in many cultures, the

Kaliai see death not as the end of all experience, but as a transition between

different life states – a transition that takes time and is aided by funerary

rituals. Later life is seen as a progressive severing of ties and putting one’s

house in order. When the Kaliai sense that death is coming they try to ward

it off, to give time for bringing social relationships to a close. In the idealized

good death, the dying person ‘‘called all his kinsmen to gather around him,

disposed of his possessions after repaying the obligations owed by him and

forgiving any obligations of others to him, and then informed those gath-

ered that it was time for him to die’’ (Counts, 1976–1977, p. 370).15 There

are no mortuary rites for a good death – all is appropriate and nothing needs

to be ‘made right’ through ritual intervention. In Kaliai, however, there are

virtually no ‘good death’s, which means that almost everyone has a funeral.

The good death in Kaliai, as the good death in terms of the disengage-

ment theory of aging, is one in which the individual times withdrawal from

society to coincide with actual biological decline and cessation. The indi-

vidual ceases to be a social being at the same time that he or she ceases to be

a living biological being.16 The ‘good death’ has been little studied in west-

ern societies but in the terminology of the life course perspective, a good

death would mean a correspondence between objective and subjective as-

pects of the career (Marshall, 1978–1979) in which, as with other transitions,

rituals may be required in order to bring about this concordance in the

biographical experience of individuals and in the social definition of the

transition by members of that individual’s social group.

It is not just that cultural ideals or schemas of the good death influence

people’s action; their social and collective actions develop such cultural

ideals or schemas. In the retirement village (or congregate living complex for

older people)17 that I studied, the residents, or at least a leadership group

among them, recognized that they were all in the final trajectory of their

lives, one that leads to death. I drew on Berger and Luckmann’s concept of

the legitimating function of symbolic universes for this analysis. As they

noted in their classic book, The Social Construction of Reality (Berger &

Luckmann, 1966, p. 101), ‘‘A strategic legitimating function of symbolic


universes for individual biography is the ‘location’ of death. The experience

of the death of others and, subsequently, the anticipation of one’s own death

posit the marginal situation par excellence for the individual’’. In this re-

tirement village (pseudonym Glen Brae), I gathered persuasive evidence that

the residents were highly accepting of the frequency of death in this com-

munity of old people, and also of their own impending death (Marshall,

1975a, b). However, in a nursing home (pseudonym, St. Joseph’s Home)

that I studied at the same time, there was much less evidence of the ac-

ceptance of death (Marshall, 1975b).

In the retirement village, the administration had not made plans for the

management of dying and death as a community event, but by the time the

community had been in existence for only one year, the residents had begun

to organize as a community of the dying. An editorial in the newsletter

published by the ‘residents’ forum’ is worth quoting at length. It noted that

life was taking on a new meaning at Glen Brae,

And new responsibilities are ours too. Fifteen deaths have occurred to date, which was

the predicted actuarial rate. The rate will increase as we grow older. With 100 new

residents arriving next year, it is forecast that we can expect a death amongst us as

frequently as one every two weeks. This is a sober thought.

Our responsibility, therefore, involves a point of view, a determination. Either Glen Brae

will turn into a place shrouded in a funeral parlor atmosphere of tears and perpetual

sadness, or it will play its intended role – the best place to be when crises occur. It is

suggested that each of us look toward the future and be prepared, that we respect the

faith of others, the wishes of the survivor, and above all else that we reduce to a

minimum the prolongation of sorrow, the discussion of pain, loss, tragedy. It is up to us,

not management, to make Glen Brae the haven we desire.

Deaths at Glen Brae are acknowledged by a brief bulletin-board notice and

a name-only listing in the newspaper. Funerals are held off-site. There is an

infirmary at the facility and many resident deaths occur there, thus reducing

the visibility of death. At congregate mealtimes, the hostess sometimes di-

rected sympathetic residents to the tables of recently bereaved residents. In a

number of ways, then, the residents developed a culture internal to their

community, which gave them a set of guidelines and procedures to handle

the frequent occurrence of death among them.

In contrast, at St. Joseph’s Home, where death was an equally frequent

visitor, I found no resident-developed cultural beliefs or organizational

procedures that suggested they had themselves taken control of this final

stage of their life course. This was consistent with a general organizational

contrast between it and the retirement village (Marshall, 1975b). At Glen

Brae there was little administrative intervention into the lives of residents;


St. Joseph’s much more strongly represented a total institution in Goffman’s

(1961) terminology, where the whole round of life was organized by the

administration in ways that limited spontaneity and control by the residents

over their lives. Time was more regulated. For example, residents lined up

outside the dining hall and entered when a bell was rung. At St. Joseph’s the

term ‘dying’ seems to be reserved for the very last stages. As one nursing

aide noted, ‘‘When they start dying, they don’t last for but a day. Some of

them take longer’’. However, the trajectory toward death begins earlier and

is marked by physical movement from a dormitory or private room on an

upper-level floor to an infirmary on the main level. Then, if or when the

prognosis of ‘dying’ is made, the resident is moved to a special room, which

is referred to by staff and residents as ‘the dying room’, but also known to

both as ‘St. Peter’s Room’. In a Catholic institution this clearly signifies the

last stage prior to death and, ironically, this is also Room 13.

Residents are acutely aware of this trajectory. One resident moved to

St. Peter’s room with a ‘dying’ prognosis returned on his own accord to his

old room in the middle of the night. Another resident, not ‘dying’ but placed

in the room when it was vacant because he had been ‘acting out’ and dis-

turbing roommates, also abruptly left the room when he realized where he

was. Almost all of the 15–20 deaths per year were commemorated by fu-

nerals at St. Joseph’s chapel, with an open coffin and at which the priest

deliberately delivered the funeral oration in a booming voice that could be

heard throughout the home. The death of residents is also more visible in the

nursing home than the retirement community because residents eat every

meal at fixed dining hall places, marked by a name card. In my observation,

these name cards, and the empty place, remained for some time following a

resident’s death.

It might be said that people move to the retirement community, but have

organized a collective way to live with the fact that they are a community of

the dying as well as the living; but that the nursing home is a place where

people go to die, where an important aspect of daily life centers around the

fact that people die there, and where death receives considerable ritual

treatment. In some senses, judging from a staff-resident interaction patterns

and the low level of visitation of residents by family members or friends,

many of the nursing home residents were ‘socially dead’, to use Kalish’s

(1966) terminology:

Social death occurs when an individual is thought of as dead and treated as dead,

although he remains medically and legally alive. Any given person may be socially dead

to one individual, to many individuals, or to virtually everyone, and perhaps to himself

as well.


Referring back to my earlier treatment of agency as delegation – authorizing

parties to act on behalf of something or someone, including themselves –

agency is not delegated to residents of St. Joseph’s home. Decisions are

made for them more than by them. Kalish elsewhere notes that ‘‘The self-

perceived socially dead individual has accepted the notion that he is ‘as good

as dead’, or that he is, for all practical purposes, dead’’: (Kalish, 1966) Such

characterizations reflect the social construction of the life course, which

creates life course trajectory categories through which some segments of the

aging population will pass. The contrast between these two social settings

shows the differentiation of the life course and the fact that its organiza-

tional properties and biographical experience by individuals can vary

greatly.

In summary on this point, the research question was, Is there a shared

conception of what is a good life course? I approached this very narrowly in

terms of the concept of the good death. There is cultural and subcultural

variability in concepts of how people should end their life course in death.

This can be seen cross-culturally and within the subcultures of two res-

idential facilities for the aged in the same geographical area, but which

differed by social class, religion, and many other characteristics. But the

comparison also says something about agency and social structure. The

social organization and administration of the two communities reflected

extreme positions with respect to structural barriers to social action. The

two community populations differed greatly in both human and social cap-

ital (e.g., in education, socioeconomic status, and viable family and friend-

ship networks), and these differences were reflected in the administrative

stances of the administrations of the two communities. At Glen Brae, social

action by residents was encouraged by administrative principles and prac-

tices; at St. Joseph’s home, it was not. It would have been much more

difficult to organize around the social construction of the last trajectory of

the life course in the latter milieu than in the former. Formulated much

more abstractly and extending from this example to other aspects of the life

course, my answer to the research question is three-fold.

First, let me return to the general conceptual discussion in the first half of

this paper. The issue is framed as one of culture, which I take to mean the

beliefs and values shared by the members of a collectivity. Cultures are

schemas developed by social groups and communities. Together with re-

sources, they constitute social realities, which have their own existence over

and above those of the individuals who came together to construct them,

and this (rules and resources) is social structure, with its potential to either

liberate or constrain social action.


Second, there is cultural variability in conceptions of the good life course;

this variability may lead to conflicts between constituencies – societies,

communities, interest groups – with different idealized views of the life

course.

Third, social structures, such as the organizational features of the two

settings I studied, can have a broader impact in either constraining or fa-

cilitating social action. In a longer time frame, some social structures rec-

ognize the agency of societal or group members, and this can produce new

social institutions and structures (shared meanings, schema, and values plus

patterns of social behavior and accompanying resources) that meet member

needs. Other social structures place agency in the hands of a minority such

as the administrators at St. Joseph’s home, and fail to reward social action

by members. In terms of this research question, it is not simply a case of the

existence or not of conceptions of the good life course, but rather it is also a

case of the existence of structural opportunities or constraints on both the

social construction of such life courses and the realization of these life

courses in individual biographies.

4.3. Operationalizing the Life Course

The second, methodological, question asks how the different concepts (such

as trajectory, stage, transition, and event) are usually operationalized, and

with what consequences. I will examine this with respect to the comparison

of ‘objective’ with ‘subjective’ data about the dying trajectory. The previous

discussion has shown how the objective dying trajectory was differentially

constructed in different societies and in the two settings in the United States

that I had studied. Here I want to first introduce the notion of differential

objective and subjective life courses among those near to death, and then to

describe a situation in which objective and subjective realities conflict, with

intriguing social consequences.

Just as the changing nature of work and retirement has dramatically

altered the objective nature of the working life course, as well as its bio-

graphical or subjective experience through the adult years and the transition

to retirement (Marshall, Heinz, Kruger, & Verma, 2001), changes in life

expectancy and life expectancy in good health have dramatically altered

health transitions and the transition from life to death – both objectively

and subjectively. Objectively, people are living longer and could expect to do

so. I thought it would be interesting to ask people how long they expected to


live. The findings point to the subjectivity of such responses and to the

importance of social psychological comparison processes.

I asked respondents at Glen Brae how old they would live to be in three

different ways, with varying success. Of 79 respondents, only 31 gave a

definitive answer to a direct question, ‘‘How old do you think you will live to

be’’; 60 placed a mark on a line to indicate where they saw themselves

between birth and death; and 50 answered a four-alternative question,

‘‘Which one of these would you say about your own future?’’, ranging from

‘‘I shall be around for some time yet; more than ten years’’ to ‘‘The end may

be any time now’’. All indicators correlated quite strongly with age, but

there was a lot more going on in their estimates of what I called ‘‘awareness

of finitude’’ (Marshall, 1975c). Focusing on the third, fixed alternative

measure, responses to it could be better predicted in relation to a variable

describing whether the respondent was currently younger than the age at

death of both parents, one parent or none. That relationship was also much

stronger for men than women. Another strong predictor was the number of

dead brothers or sisters. Qualitative data also suggested that awareness of

finitude is higher (i.e., anticipated life expectancy is shorter) with the death

of friends. Awareness of finitude is thus a partial function of age, but it is

also influenced by a number of social comparison processes, and the

conviction (which is of course supported by data) that ‘longevity runs in

families’.

This said, it is also the case that many Glen Brae respondents found

themselves in the position of having lived beyond the age they had antic-

ipated. Here the objective life course and the subjective life course were at

odds. In the particular living circumstances of this retirement community,

this created problems for many residents – problems made more complex

because the individual residents were not the only ones with a stake in the

accuracy of these estimates. Glen Brae was one of the early examples of a

retirement community organized on the ‘life care principle’. On entry, a

resident would pay a founder’s fee that guaranteed his or her right to remain

in the retirement community for life, paying rents that would be set by the

corporation. Since the founder’s fee was quite significant, a rational person

would not move to this community unless he or she expected to live long

enough to make this a good investment. In addition, the resident had to be

satisfied that, after making the substantial founder’s fee payment on entry,

he or she had enough assets to generate a revenue base to pay the monthly

rental fees as long as they lived. On the other hand, the administration of

Glen Brae used the founder’s fees to amortize the large mortgage on the

new, costly physical plant, while counting on monthly rental fees to meet


operating expenses. Once all units in Glen Brae were occupied, the only way

for the corporation to generate funds to pay the mortgage was through new

founder’s fees, but these could be generated only when a resident died and

the apartment became vacant. Thus, in an abstract sense, two parties –

resident and administration – were making bets based on life-expectancy

estimates.

I examined this situation from the perspective of game theory, and found

that both parties had made significant errors in the life estimates. As noted

earlier, residents made subjective estimates of their life expectancy based on

their age and other social comparisons. They mostly neglected to take into

account general cohort increases in life expectancy, and cohort differences in

socioeconomic status, which correlate with health and life expectancy. In

other words, they were not good life course theorists in that they ignored

cohort factors. (I did not characterize this in life course terms at the time,

but it is a good example of one of the most basic of life course principles).

The corporation, on the other hand, used age alone. Rather, it relied on the

insurance company that held the mortgage to estimate turnover of the

apartment units, and that company did so based on life-expectancy tables

for its insurance policy holders. However, it did not take into account two

things: first that the typical resident of Glen Brae is of a higher social class,

and therefore likely to live longer than the typical life insurance policy-

holder; and second, that the person moving to the retirement community

may be thought of as acting in a different strategic or decision-making mode

than the typical insurance policyholder. The former may be seen to be

making a very strategic bet to live long enough to make this a good in-

vestment, and people who think they will not live very long may very well

opt not to pay that expensive founder’s fee.

To cast this situation as a game is to go well beyond the data, reconstructing

actions of the administration and residents in terms of imputed rationality.

However, the exercise calls attention to a few points related to the research

question I have been discussing. That question, which is put to us as a meth-

odological question, asks, how are different concepts in the life course opera-

tionalized in different disciplines, and with what consequences? As with the first

question (and the next), I am addressing this very narrowly with respect to two

concepts – trajectory, and that troublesome conceptual relationship between

agency and social structure.

The methodology here is principally in my crude attempt to measure and

to understand the anticipated final trajectory or, more precisely, its length or

duration; i.e., awareness of finitude. In other aspects of my research on

aging and dying, I used awareness of finitude as an independent variable, to


assess as best I could its causal relationship to other phenomena, such as

concerns about death and dying, time perspective, and the extent to which

the individual is preoccupied with self and identity issues. These investiga-

tions, which I cannot go into here, and those just reported, which try to

explain how people come to make estimates of anticipated life expectancy,

all help us to understand the concept of awareness of finitude (through

construct validity). In terms of the life course perspective per se, there has

been less work done on the anticipated life course than on the experienced

life course (see Markus & Nurius, 1986; Ryff, 1991; Mueller, 2002). The

general points I would make with respect to this methodological question

are:

First, the distinction between objective and subjective aspects of trajec-

tories should be maintained and investigated, and discrepancies between the

two are likely to be theoretically interesting – as work on Neugarten’s notion

of ‘social clocks’ attests (Neugarten & Hagestad, 1976; Ryff, 1991). Antic-

ipated and realized careers or trajectories are ideally investigated with lon-

gitudinal data (Mueller, 2002), and there are great concerns that

retrospective data will be biased through self-serving reconstruction. How-

ever, reconstructions of the past as well as changing constructions of the

future are core materials in studies of the life course.

Second, and more generally, there can be multiple objective and subjec-

tive life courses ‘in play’ at the same time, and objective life courses can

include largely biological aspects of the life course including physiological

changes such as puberty, menopause, physiological declines, and finally

death.18

Third, the investigations I have described, and particularly the use

of game theory, raises questions about limits to Clausen’s (1991) concept of

planful competence, discussed earlier under agency. As noted earlier,

‘planful competence’ is the ability to make informed, rational decisions

and set realistic short- and long-term goals. I suggested earlier that this can

be seen as agency in this first sense of the metaphor – the existence of

the furnace, and it can be viewed as a developmental capacity of (virtually)

all humans. However, using game theoretical analysis is a methodological

trick to show the difference between capacity and outcome or, in my

preferred terminology, between agency and action. Life courses are far

from ‘rational’ in any pure sense, and related to this departure from ra-

tionality is the fact that people live their life courses socially, negotiating

their way through life with other actors also exercising planful com-

petence, but with often-conflicting definitions of the situation, goals, and

resources.


4.4. Status Changes and Changes in Identity

The third, results question asks to what extent changes in status result in the

redefinition of identity. I will address this question by referring to the dis-

tinction between self and identity, and to the action of the aging and dying

individual, in managing identity. The question begs for a definition of

identity, which is a term used in many different ways in the social sciences.19

In my work, identity is that sense of sameness and continuity in the

organization of one’s selves over time. Following Goffman (1963) I also

distinguish between ego of felt identity, imputed or social identity, and

personal or presented identity. The former refers to the individual’s own

sense of selves over time, the second to the views of others, and the third to

the presented self (which as Goffman so well shows, does not always cor-

respond closely to the former and is a way in which people seek to influence

others to change the identity they impute to them).

At the social or collective level, I described above how the residents at

Glen Brae sought explicitly to shape their social identity. The residents had,

in fact, changed their social status when they moved to the retirement com-

munity, which, as a ‘life-care community’ they anticipated would be their

collective home until each died. But they recognized options in how they

were to view themselves and collectively committed to an option that fo-

cused on themselves as living, rather than as dying. There are good social

psychological concepts to deal with this phenomenon, of which the concept

of role distance is as insightful as it is difficult to measure (Goffman, 1961).

Role distance is setting a distance between one’s self and a specific role. It is

a way of remaining more than the specific role one is playing at the time. At

Glen Brae, residents collectively took the position, ‘I know I am dying but

my whole being is not wrapped up in that. I am many other things than a

dying person’.

At a more individual level, I was interested in my early research in how

the recognition of the self as dying leads to changes in ego or felt identity.

This is the metaphor of the individual in the last chapters of life (Marshall,

1980). Long ago, the hermeneutic scholar Wilhelm Dilthey described the

selectivity of autobiographical reflection as follows:

The person who seeks the connective threads in the history of his life has already, from

different points of view, created a coherence in that lifey. He has created it by ex-

periencing values and realizing purposes in his life, making plans for it, seeing his past in

terms of development and his future as the shaping of his lifey. He has in his memory,

singled out and accentuated the moments which he experienced as significant; others he

has allowed to sink into forgetfulness (Dilthey, 1962, p. 86).


To summarize my findings briefly, I found a relationship between awareness

of finitude, and reminiscence. While scholars such as Erikson (1959) and

Butler (1963) had focused on awareness of finitude as initiating focused or

intense individual reminiscence and reconstruction of one’s identity, I found

a strong relationship with social reminiscence as well. Asked, ‘‘Do you often

talk about things that have happened in your past life with anybody else?,

36% of respondents estimating more than ten years to live, 32% of those

estimating five to ten years, but fully 62% of those estimating less than five

years said they do so ‘at least once a week’, rather than less frequently

(Marshall, 1975c, p. 125). Higher social reminiscence was associated with a

greater intensity of reminiscence (they reported more turning points when

asked to describe their lives), with reporting turning points that were coded

as reflecting internal rather than external locus of control (things they did

rather than things that happened to them), and with a greater likelihood of

reporting satisfaction with their life as a whole (summarized in Marshall,

1980, pp. 115–119).

It is impossible to sort out causation from these cross-sectional data. Only

a longitudinal study could establish a clear temporal relationship among

changes in awareness of finitude, changes in the extent to which the indi-

vidual engages in individual or social reminiscence, and changes in the con-

tent of the remembered biography as well as satisfaction with the biography

as a whole. My cross-sectional data let me talk about differences but not

changes. My interpretation that social reminiscence assists the individual in

the reconstruction of biography leans more strongly on theory than on my

data. Moreover, in attending to social in addition to individual reminis-

cence, it contrasts with more individualistic, psychological approaches to

biographical narrative, such as those described in McAdam, this volume.

Let me return to the third question, about results, is it possible to dem-

onstrate that a change in status implies a redefinition of the identities of the

persons involved? From my limited study of three decades ago, I infer the

following answer to this question:

First, life course scholars should devote theoretical and methodological

attention to the complexity of identity. If a person has as many selves as

there are groups about whose opinion he or she cares, then a concept of

identity is needed to deal with how the individual prioritizes, situates,

manages, and organizes these many selves so as to give some sense of

sameness and continuity over time. We need first to distinguish self from

identity.

Second, we need to distinguish identity as experienced by the individual

from identity as attributed to the individual by various groups of audiences.


Third, it is easier to describe differences in identity than to explain

changes in identity, and we would all benefit from a sustained attempt to

theorize the mechanisms of identity change. This task calls for multidisci-

plinary skills because these processes, my work suggests, take place at bi-

ological, psychological, and social levels, and the intersection or interaction

of these levels.

5. AGENCY, EVENTS, AND STRUCTURE AT THE END

OF THE LIFE COURSE

I want to quickly conclude with some general remarks based on my visit to

earlier research that I conducted more than three decades ago. I have tried

not to reconstruct the language of this work, while at the same time showing

its relevance to contemporary issues of the life course.

Perhaps, the major conclusion of this exercise is that it reminds us of the

long chains of scholarship that have contributed to what we now think of as

the life course perspective; and perhaps in addition we can be reminded that

the life course perspective of today is highly diverse. I have addressed both

these issues elsewhere (Marshall & Mueller, 2003), focusing on the similar-

ities and differences between North American and European approaches to

the life course.

In some senses I came to the ‘life course perspective’ late, but in other

senses I came to it early. Many theoretical strands come together to form

what is now the life course approach, and I had made use of some of these

strands. Some of them, grounded in symbolic interactionism, gave us con-

cepts like career and status passage that gained currency in parts of Europe,

but were largely supplanted in North America by the terminology subse-

quently introduced by Riley, Elder, and their associates. Status passages and

careers became transitions and trajectories in most American scholarship,

and increasingly sophisticated statistical techniques were applied to analyses

that paid more attention to complex descriptions of the life course than to

how people actually made their way – as individuals and in collective action

– through life courses that they were also helping to construct. The concept

‘agency’ came to be a catch-all phrase that most of the time simply paid

respect to the human capacity for voluntaristic action, and told the reader

that the author was not a complete determinist (despite using deterministic

statistical methods). Agency often meant, ‘unexplained variance’ – if the


model did not explain much variance, then this must mean that people were

making innovative choices or struggling against structural barriers.

Revisiting my early, ‘pre-life course perspective’ scholarship, I did not feel

uncomfortable with the symbolic interactional and phenomenological sources

and language I had used. Instead, I think this approach to life course the-

orizing made it easier than the more orthodox language that predominates

today, to address the negotiated character of life course construction and

transitions. I hope that the Europeans will reinvigorate international life course

theorizing by continuing to make good use of the life course–concepts they

initially borrowed from Chicago school sociology.

Revisiting my early work produced a stronger sense than I would have

anticipated of the importance of bringing together objective and subjective

aspects of the life course. When cultural ideas of the life course are com-

bined with resource allocation mechanisms and resources, we have social

structure, which is objective at the macro level. Individuals then create,

experience, and endure their life courses in relation to these objective di-

mensions of social structure. As they do so they may change the social

structure or they may reinforce it, by submitting to its constraints or cap-

italizing on the opportunities it provides (these two together constitute life

chances). Their careers or life courses may turn out to be some jointly

produced product of objective, structural constraints, and opportunities, the

assets and resources they bring to the choices they make, and the micro-level

interactions they have with others who, like them, are trying to create,

experience, and endure their own life courses. A given individual’s vision of

his or her life course may be realistic or not in terms of objective or struc-

tural life course conditions. It may coincide, conflict, or synergize with that

of other members of his or her social group or collectivity and this might in

turn influence the nature of the attained life course. In any case, people will

experience their own objective life courses, the dimensions of which can be

measured and analyzed in life course research. But the full nature of their

life experiences includes a subjective life course as well, one with anticipat-

ions and plans, but also memories and reconstructions or reinterpretations

of the lives they have led.

NOTES

1. Another beginning point might well be the work of the late Klaus Riegel (e.g.,Riegel, 1975, 1976; see Lerner & Busch-Rossnagel, 1981b for a brief discussion of hiscontributions in this area).


2. The work of Piaget is grounded in, and empirically supports, such a notion,with the individual seen as actively seeking novelty so that, through accommodation,schemata will change so as to permit assimilation, enabling increasing mastery ofone’s environment.3. It may be instructive to examine creativity across the life course in light of

agency and developmental theory. See Kastenbaum (1992).4. On this point he is criticized by Dannefer (1999).5. At this point Heinz is in fact drawing on Giddens (1984), but elsewhere in

Giddens, e.g., 1991b) the notion of agency is much more broadly drawn. I discussthis below.6. This conceptualization seems to include agency as an aspect of human nature.

However, in the same article, Dannefer treats agency as a variable. Elaborating on acase described by Gubrium, Holstein, and Buckholdt (1994), of a child labeled as lowin ability and placed in the lowest of three ability groups, Dannefer shows howlabeling creates a self-fulfilling prophecy that keeps this child in his or her place. Hereis how Dannefer interprets this situation:

Even when the outcome is positiveywhat is being described here is a social system

process in which the labeled individual participates in a subordinate manner. Thus, the

agentic force of the individuals in question, including the positively labeled individual is,

in principle, no more in evidence in the case of positive than negative labeling. This raises

the question of whether and how genuine human agency might be augmented – a

question that, in any domain, is ultimately political. (Dannefer, 1999, p. 78).

7. Giddens (1991a, p. 112) contrasts ‘fateful moments’ with daily routines of life.‘‘Fateful moments are those when individuals are called on to take decisions that areparticularly consequential for their ambitions or more generally for their future lives.Fateful moments are highly consequential for a person’s destiny’’.8. I limit the statement as this is not the place to consider whether persons born

with limited or no cognitive capacity are fully human.9. This task is related to, but quite distinct from, the conceptual and method-

ological challenges of linking ‘micro’ and ‘macro’ levels of analysis; where challengesare addressed absent the notion of individual agency. See, for example, Collins(1992); Heinz (1996); Marshall (1995) and O’Rand and Campbell (1999).10. Alwin (1995, p. 217) observes of a panel that tried to define it: ‘‘There was little

theoretical consensus on what structure was and a confusion of meanings, but virtuallyeveryone agreed that the conceptual apparatus conveyed by the concept of socialstructure was what made that sociological contribution to the study of individual livesa possibility’’. The same cannot be said of agency or even of action, as there aresociologists, such as some structuralists, who argue such concepts are not needed.11. Going beyond Giddens’ social structure as virtual ‘rules’ and ‘resources’,

Sewell (1992, p. 8) ‘‘would in fact argue that publicly fixed codifications of rules areactual rather than virtual and should be regarded as resources rather than as rules inGiddens’ sense’’.12. While she postulates this view as against Giddens, Berger and Luckmann,

there are many similarities to both. As for possible differences, Archer sees a con-stant possibility for emergence, from unanticipated consequences of social action,


but also from the innovative nature of individuals. The similarity to Schutz is ap-parent in the following: ‘‘The activity-dependence of structures is in no way com-promised by the argument that a given structure was issued in by a particulargeneration/cohort of actors as an unintended yet emergent consequence of theiractivities, whilst it then necessarily pre-existed their successors. This is the humancondition, to be born into a social context (of language, beliefs, and organization)which was not of our making: agential power is always restricted to re-making,whether this be reproducing or transforming our social inheritance.’’ (Archer, 1995,p. 72). Because Giddens’ structuration theory denies the existence of emergence, andalso (p. 87) because Giddens is interested in a short time frame, Archer argues thatstructuration theory has ‘‘an inability to examine the interplay between structure andagency over longer temporal tracts because the two presuppose one another so close-ly’’. Archer’s desire to make time an actual component in the theory may be comparedto the importance of time in most life course perspective approaches, such as Elder.13. I cannot do justice to Sewell’s full argument here, including his use of

Bourdieu, but I think he has given the most satisfactory conceptualization to theagency-structure discussion in current sociological theorizing. In my own thinking Iwould draw further on Berger and Luckmann and Archer (whom Sewell does notresource).14. Data were collected through participant observation, interviewing, and ar-

chival methods as my doctoral dissertation research. I was in the field in these twosettings over the period 1969–1970. The bulk of my data were based on fieldwork andsemi-structured interviews in the retirement community, where I completed at leastone lengthy focused interview with 105 of just under 400 residents. Of these,I interviewed 68 respondents three times, and 92 respondents twice. In the nursinghome, I conducted fieldwork over a 3-month period, mostly using participantobservation, but also unsystematic focused interviews.15. The Limbu of Nepal (Jones, 1974) and the LoDagaa of West Africa (Goody,

1962, pp. 208–209) have a similar conception of the good death and Meyerhoff(1978) describes the dying of an elderly Jewish American in a similar light (Marshall,1980, pp. 33–34, 151–152).16. Hertz (1960, p. 76) notes that in many parts of the world, deaths of children,

strangers and slaves ‘‘arouse no emotion, occasion no ritual’’, and with respect to theaged, he says, ‘‘In various Australian tribes, old people who, because of their greatage, are incapable of taking part in the totemic ceremonies, who have lost theiraptitude for sacred functions, are buried immediately after deathy. This is so be-cause, due to the weakening of their faculties, they have ceased to participate in sociallife; their death merely consecrates an exclusion from society which has in fact alreadybeen completed, and which every one has had time to get used to’’ (Hertz, 1960, p. 85).17. Current terminology in the USA for such a facility would be ‘‘Continuing

Care Retirement Community’’.18. I am not able to address the importance of catastrophe and diversity in life

courses. Many things simply cannot be predicted and these range from major eventssuch as wars, terrorist attacks and natural disasters to individual-level events such asmotor vehicle accidents and heart attacks. Linda George (2003) in a recent essay on lifecourse perspectives in the area of aging and health, notes that health traumas oftenlead to divergence in life course pathways. She also reminds us of the tremendous


divergence of pathways in the absence of trauma, as noted, for example, in the classicstudy by Rindfuss, Swicegood, and Rosenfeld (1987).19. Often the terms self and identity are used interchangeably. I argue that it is

important to distinguish the two. Self, drawing on symbolic interactionism, is theprocess of reflection and cognitive orientation in which the I responds to the self – asobject (the me) based on both internal cues and external feedback from others.Fol-lowing James, I have as many selves as there are groups of people about whom Icare. These many selves develop in relations with others in role relationships andindividual selves become situated in social relationships with (types of) others.However, some selves are more important than others, and moreover the socialscientist needs to be able to account for the individual’s ability to select and presentselves.

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92

LOOKING AT AMBIVALENCES:

THE CONTRIBUTION OF A

‘‘NEW-OLD’’ VIEW OF

INTERGENERATIONAL

RELATIONS TO THE STUDY OF

THE LIFE COURSE

Kurt Luscher

1. INTRODUCTION

This chapter has its origins in the kind invitation to present, at the PaVie-

Colloquium, an idea that is receiving increasing attention in the study of

intergenerational relations. Its essence can be summarized in the following

hypothesis: Intergenerational relationships, especially among adult children

and their parents, imply the experience of ambivalences and, consequently,

require dealing with ambivalences.1 Thus, my point of departure does not

seem to be a major issue of life course research. However, at second glance,

one may recall that embeddedness in intergenerational relations is crucial

for personal development. Most human beings are conceived in and born

into familial contexts, and parent–child relationships – as diverse as they

may be – are in many ways important for the unfolding of personal abilities





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93

and the consciousness of the self. Youth is a formative phase in the life

course where intergenerational relationships are of importance, simply be-

cause their dominance may be challenged by other relationships, such as

those among siblings and peers. This is also true for early adulthood. Later,

through partnerships and marriages, and thus the acquisition of in-laws,

there is an increase in the number of elders with whom close and intimate

relationships become possible or are even expected and required. In mid-life,

nowadays, most adults belong to genealogical networks involving three or

even four generations. Later, obligations for the care of the very old may

emerge. The rules and the practice of inheritance once more accentuate the

social and material importance of intergenerational relationships and their

impact for the conduct of personal lives. In addition, the institution of

inheritance reminds us that any life course may also be comprehended as a

link in a chain of generations.

Indeed, the study of the life course may profit from taking into account

the interplay with the study of intergenerational relationships, and conse-

quently from recent developments in this field. To this obvious statement,

I would like to add two points. First, because of their omnipresence, in-

tergenerational relations are at the core of the processes of socialization and

of human sociability. This is why insights from the study of intergenera-

tional relationships are of foremost interest for the analysis of social rela-

tionships in general, be it with regard to what they have in common with

other relationships, or to where they differ from them, for instance from

market relationships. My second remark is meta-theoretical. Because of the

great relevance of intergenerational relations, their understanding is usually

bound to moral judgments. Such normative views often penetrate scholarly

descriptions. For instance, it is quite common to idealize intergenerational

relations – positively – with reference to the concept of solidarity, or to

deplore them – negatively – as a notorious source of conflict. As I will show,

a well-grounded theory of ambivalence allows us to overcome these biases,

because it simultaneously takes into account and analyzes both perspectives.

In this way, a high degree of social authenticity can be achieved, and re-

spective normative orientations can become a deliberate topic of analysis.

Moreover, we recall that general assumptions about human nature underlie

the concepts used in social science research, especially about such funda-

mental issues as the conduct of human lives and their social organization.

However, at this point I cannot present a comprehensive account of the

importance of ambivalence for the study of the life course. I must limit

myself to outlining the meanings of this concept as such, and I will present

the conceptual frame that I and other researchers have developed. Taking

KURT LUSCHER94

this as a point of reference, I will also illustrate the usefulness of this ap-

proach by presenting some exemplary results of recent research. I shall

concentrate on issues closely related to the study of the life course and of

connected lives, and I will suggest further applications in this field.

2. AMBIVALENCE IN INTERGENERATIONAL

RELATIONS: THE REDISCOVERY OF

AN OLD EXPERIENCE

The idea of drawing on the concept of ambivalence for the study of

intergenerational relationships has two sources. First, an awareness of the

usefulness of ambivalence as a theoretical concept arose from a critical eval-

uation of the existing literature on intergenerational relationships, which in

the 1990s was aptly characterized as data-rich and theory-poor (Luscher &

Pillemer, 1998). In particular, we criticized the dominance of the so-called

solidarity perspective, because it presents a picture of intergenerational re-

lationships that pays too much attention to positive aspects and too little

to the innately darker ones. The solidarity perspective arose in reaction to

Talcott Parsons’s (1942, 1949) portrayal of the nuclear family as isolated. It

holds that, to the contrary, extensive family solidarity does exist. (Shanas et

al., 1968; Littwak, 1965; Sussman, 1959). Since the early 1970s, Bengtson and

co-workers have continued to develop this approach in an influential series of

articles and books (cf. Roberts, Richards, & Bengtson, 1991; Bengtson

& Harootyan, 1994; Bengtson, Giarusso, Mabry, & Silverstein, 2002). The

solidarity perspective has also been adopted by other researchers in the Unit-

ed States (Rein, 1994; Rossi & Rossi, 1990) and serves as a reference point for

many European authors, although not without critical reservations (Attias-

Donfut, 1995; Bawin-Legros, Gauthier, & Strassen, 1995; Donati, 1995;

Finch & Mason, 1993; Szydlik, 2000). However, at the same time as scholars

in the solidarity tradition have emphasized mutual support and value con-

sensus, another line of research has focused on isolation, caregiver stress,

family problems, conflict and abuse (Marshall, Matthews, & Rosenthal,

1993). The image of weakened family ties and the abandonment of the

elderly continues to be widely held in popular opinion and in portrayals of

the family in contemporary fiction and theater. Thus, some scholars, as well

as the public at large, appear reluctant to accept that intergenerational re-

lationships include solidarity and are characterized by shared values and

reciprocal help. As Marshall et al. (1993, p. 47) have succinctly put it, ‘‘the

Looking at Ambivalences 95

substantive preoccupations in gerontology over the past 30 years point to a

love–hate relationship with the family.’’ In a somewhat different mode,

Lalive d0Epinay and Bickel (1994), summarizing their comprehensive

description of the aged and their familial networks in Switzerland, refer to

the tensions created between the potentials of family solidarity and the

limitations imposed by contemporary social conditions. In view of such

accounts, Karl Pillemer and I have proposed that the study of parent–child

relations in later life must move beyond a ‘‘love–hate relationship’’ (Luscher

& Pillemer, 1998). The vacillation between images of mistreatment and ne-

glect, on the one hand, and comforting images of solidarity, on the other,

are not two sides of an academic argument that will ultimately be resolved in

favor of one viewpoint.

Second, parallel to this theoretical evaluation, we conducted a research

project at the University of Konstanz on the reorganization of families after

divorce in later life, e.g. an important event in the life course (Luscher &

Pajung-Bilger, 1998). Data were collected in semi-structured interviews with

103 persons in 65 families. These interviews included questions about the

way all the subjects experience intergenerational relations. Our goal was to

distinguish different degrees of mutual solidarity in the aftermath of what

in many cases represents a ‘‘turning point’’ in the lives of the individuals

involved and their experience of intimate relationships. Yet, even a differ-

entiation in terms of everyday concerns, and by content and types of re-

lationships, did not yield conclusive results regarding the relevance of

solidarity. Family members reported both instances of support and of

neglect. This led us to search for a concept with which we could take into

account the existence of both solidarity and conflict in the process and the

understanding of intergenerational relations. The notion of ambivalence in

the everyday sense (being torn in two directions) was a first and natural

choice.

In the course of work along these lines, we became aware, however, that

references to the experience of ambivalence in social relationships, and es-

pecially in personal relationships, which involve dependency and intimacy,

have long been a topic of popular wisdom and of literary writings, even

before the term existed. Indeed, insights into what we call in modern lan-

guage ‘‘ambivalence’’ between parents and adult children can be traced back

to the beginnings of human society. In Greek mythology, some of the

greatest sagas depict what we now refer to as ambivalence. The best known

of these is the tragic drama of the relationship between Oedipus and his

father and mother. Reinharz (1986) gives an informative overview on ‘‘lov-

ing and hating one’s elders’’ as ‘‘twin themes in legend and literature.’’

KURT LUSCHER96

She refers, among other examples, to the tragedy of Uranus and his sons.

Hamlet as well, she tells us, can be read as a portrait of intergenerational

relations. Peter von Matt (1995) presents a comparable and very colorful

overview of the theme under the provocative title ‘‘Verkommene Sohne,

missratene Tochter’’ (Degenerate Sons, Misguided Daughters). He draws a

line from the biblical story of Absalom to the admonitory children’s book

Der Struwwelpeter (Shock-headed Peter – a classic of moralizing German

children’s literature) and recalls the complex relationships described in The-

odor Fontane’s Effie Briest and in Kafka’s tale ‘‘The Metamorphosis.’’ We

may add, as one more illustration certainly known to many readers, Philip

Roth’s novel American Pastoral as an example of ambivalence in recent

American literature.2 Furthermore, ambivalence can be seen as an ongoing

theme in the life-script or biography. Kierkegaard could serve as one of many

examples. An impressive study with ambivalence as a latent theme is Lee’s

(1998) study of generativity in the life course of the dancer Martha Graham.

In everyday life, ambivalences are often experienced, for example, in ne-

gotiations over caregiving. They can also be found by examining the overall

history of a given relationship. Seen this way, ambivalence is a conceptual

tool for evaluating specific situations, as well as for studying the develop-

ment and institutionalization of the self in the life course. This brief account

of recent approaches to the study of intergenerational relations (and given

the already-mentioned interplay: to the study of the life course) that draw

upon the idea of ambivalence illustrate why it is appropriate to speak of a

‘‘new–old perspective.’’ However, in order to become a useful tool for con-

temporary social research, a complete, detailed conceptualization is needed.

3. CONCEPTUALIZING AMBIVALENCE

3.1. Elements of a Comprehensive Definition

In the light of the foregoing, it seems reasonable to start with a brief look at

the original formulation of the term. As far as we know, ambivalence was

conceived and first introduced by the Swiss psychiatrist Eugen Bleuler

(1910) as one of four core symptoms of schizophrenia. Yet, soon thereafter

he argued that ambivalence is not merely a symptom of mental illness, but

can also be experienced and thus observed in everyday life. He distinguishes

between affective and cognitive ambivalence and points out that the two are

closely intertwined (Bleuler, 1914, p. 98). His text already contains a ref-

erence to ambivalence in intergenerational relationships (p. 103). Freud first


used the concept in an article on the theory of transference (e.g. also with

regard to social relationships!). Later, he included it in his theory of the

Oedipus complex, as is concisely and clearly demonstrated in a short essay,

‘‘Some reflections on schoolboy psychology’’ (Freud, 1914). Freud thus ap-

plied ambivalence to the analysis of an exemplary intergenerational phe-

nomenon, as well as assigning it a role in the life course.

This is not the place for a more detailed history of the concept, its re-

ception and its adaptation in different scholarly discourses. Taking into

account the major contributions and arguments in the existing literature,3

I would list the following elements as constituents of a comprehensive un-

derstanding of ambivalence:

� The experience of diametrically opposed (polarized) structures and forces

in the dynamic fields of individual (and collective) actions and respective

relationships.� The insight that these experiences are relevant for the identities (selves) of

the actors (individuals, in certain contexts also collective actors). In other

words, the experience of ambivalence and the ability to cope with it can be

understood as an aspect of human agency.� The assumption that these polarizations will be interpreted as irreconcil-

able as long as the actors belong to a certain field of action (or situation)

and are concerned, in this context, with the reflection of these tasks. This

field of action can be brief, e.g. a turning point, or extend over a longer

period of time (for instance becoming a parent).4

� The assumption that the experience of ambivalences and the ways of

dealing or coping with them can be systematically connected with the

aspects of psychological functioning, of the logic of social relations and

social structures, including the regulation of social control and power.

In view of the background of the concept’s history and its acceptance in

the social sciences, I would like to propose the following definition: For

purposes of sociological research on intergenerational relations, it is useful

to speak of ambivalence when polarized simultaneous emotions, thoughts,

volitions, social relations and structures that are considered relevant for the

constitution of individual or collective identities are (or can be) interpreted

as temporarily or even permanently irreconcilable.

Taking this attempt at a comprehensive analytical definition as a refer-

ence point, we find, in scholarly texts, two different usages. First, the term

can serve as an interpretative (or explanatory) concept. This is, in fact,

its primary use in macro-sociological texts as, for instance, in the wide-

spread characterization of ‘‘post-modernity’’ as pervaded by ambivalence.

KURT LUSCHER98

References to social reality are confined to generalizations, based mostly on

highly aggregated, generalized data. Descriptions are sometimes presented

in the form of ‘‘ideal-types’’ or ‘‘model personalities’’ such as Bauman’s

(1997) proposed ‘‘tourist’’ or ‘‘player.’’ This usage is also common in re-

search reviews, for instance in Cohler’s text about young adults ‘‘coming

out’’ as gay or lesbian and their parents (see below). Second, the concept of

ambivalence may be used as a ‘‘research construct.’’ Here, the goal is to

apply the concept in research, such as in surveys, experiments, observations

and the analysis of documents. For this purpose, an explicit definition is

necessary – one that can serve as the reference point for formulating specific

hypotheses and constructing research instruments.

We can hypothesize that people must live with ambivalences and that they

can cope with them in more or less competent, productive ways. People can

even create ambivalences, as mentioned above with regard to the works of

creative writers and artists. Deliberately constructing ambivalences can also

be a strategy in social interaction. This possibility is another reason to view

ambivalences as both opportunities and as burdens. In this regard, the un-

derstanding of ambivalence suggested here differs from other usages where –

more or less explicitly – the term bears a negative connotation. This is true,

for instance, of the term’s usage in characterizing styles of attachment be-

tween mothers and children, as well as in other typologies.

Closeness and intimacy may reinforce or strengthen the susceptibility to

ambivalence. An important precondition of ambivalence is dependency

(Smelser, 1998), which begins with birth (or even during pregnancy), con-

tinues through childhood and youth into adulthood, and in many cases even

into the later phases of the life course. It manifests itself very early in the

needs for nurture, care, protection and education. Beyond these immediate

obligations, and in the course of fulfilling them, parents develop and acquire

specific information and particular knowledge about their individual child

as a person. This knowledge reinforces the parents’ power to control and to

discipline the child, not only while he or she is young, but also in later life

phases. Over the intergenerational life course, the direction of dependency

between children, parents and older or younger generations may become

more complicated – support and care are specific instances explored in this

book. Yet the authority of older persons, established early in life, may

persist as another source of ambivalence, even as situations arise that lead to

a potential or actual reversal of dependency. Cohler and Grunebaum’s

(1981, pp. 120ff., 197ff.) studies of the relationships of mothers and daugh-

ters in Italian immigrant families provide many convincing illustrations of

this process (see below). More generally, ambivalences in the past and the


present may offer an interesting topic in the study of life reviews, both in

scholarly work (Staudinger, 1989) and in the curricula of courses offered on

practical gerontology.

The contemporary relevance of ambivalence can be deduced from a close

examination of the structural and cultural conditions of present Western

(postmodern) societies. On the macro-sociological level, population dynam-

ics have created a frame in which ambivalence easily emerges. The rise in life

expectancy, attributable to improved living conditions for increasingly large

segments of the population, was accompanied by a decrease in infant mor-

tality. As a child’s chances of survival increased, the possibility of seeing it as

an individual person also increased. A decrease in the birth rate was a logical

consequence. Childhood and youth soon came to be seen as specific phases

of the life course calling for their own institutions – for instance, public

schooling. The same observation can be made with respect to the other end

of the life course via the recognition of aging as a life stage calling for its

own institutions. The demarcation of different periods or segments of the

life course has led to a heightened consciousness of the importance of re-

lationships between age groups, or in other words, between generations.

This has been true especially in the realm of the family, and also in society as

a whole. The development of social welfare was another factor contributing

to this demarcation of life stages and of intergenerational relationships. In

many instances, structural conditions for both dependence and autonomy

were thereby created. Seen in this way, the concept of ambivalence is an-

other possibility to relate the analysis of the life course to the study of

contemporary society and the dynamic interplay of generations and their

cultural manifestations (see for example Edmunds & Turner, 2002a, b;

Blossfeld, this volume).

3.2. Proposal for a Research Module

The foregoing discussion represents a background for new applications in

research and respective operationalizations.5 The concern shared by the

study of intergenerational relations and life course analysis for the

development of personal identity (or the self) through interaction and in-

stitutionalization is a major point of reference and allows us to concom-

itantly pay attention to social relationships. This approach is compatible

with a two-dimensional view of personal identity, particularly with G.H.

Mead’s (1938) notion of the self as emerging from the interplay between ‘‘I’’

and ‘‘me,’’ where ‘‘I’’ refers to spontaneous subjectivity and ‘‘me’’ refers to

KURT LUSCHER100

generalized others or, more generally speaking, to the interplay between a

subjective and an institutional component of the self. Many interpersonal

models of personality explicitly refer to Mead. For example, Leary (who

developed a circumplex model that describes personality as located between

the poles of love vs. hate and dominance vs. submission) speaks of Mead as

a ‘‘creative watershed to which later theories of interpersonal relations can

trace their sources’’ (Leary, 1957, p. 101).

We can see in the juxtaposition between the subjective and the institu-

tional dimensions a primary condition for the experience of ambivalences. In

addition, within the module presented below, a secondary condition is sug-

gested by hypothesizing that both dimensions of an intergenerational re-

lationship, the subjective as well as the individual, can be influenced and

shaped by fundamental polarizations. Thus, the module is based on a

‘‘twofold’’ notion of ambivalence. This implies a departure from the eve-

ryday understanding of the term.

The ‘‘personal’’ or ‘‘subjective’’ dimension can be characterized as fol-

lows: Parents, children and the members of other involved generations share

a certain degree of similarity. While some of this similarity can be attributed

to biological inheritance, no inheritance is total, insofar as individual par-

ents and individual children are never genetically identical. Their similarity

is reinforced by the intimacy of interactive learning processes, which creates

a potential for closeness and subjective identification. At the same time, the

biological equipment of each organism is different. Sociologically speaking,

processes of maturation increase difference and diversity. Ultimately, chil-

dren develop different personal identities than their parents. In order to

create a schematic representation that can be used in different contexts, two

rather abstract labels are needed. To account for not only the socio-spatial,

but also for the socio-temporal aspects, we propose – for the subjective

component – the terms ‘‘convergence’’ and ‘‘divergence.’’ These two po-

larities can serve as umbrellas for a variety of attributes. Convergence

includes such relational attributes as loving, warm, solicitous, reliable

and close. Divergence is characterized as cool, easy-going, indifferent and

superficial.

For the structural–institutional component, we can conceive of a polar

opposition between a desire to preserve the traditional social forms or

structures of relationships and a desire for dramatic change. Neither is fully

realizable. For instance, although children may choose a way of organizing

their private lives that is vastly different from that customary in their family

of origin, some ties to childhood experiences may remain, even if only in

that they provide a negative background. As technical designations, taking


into account again the socio-temporal as well as the socio-spatial aspects,

the terms ‘‘reproduction’’ and ‘‘innovation’’ appear useful to express the

idea of a dynamic polarization. Here, reproduction includes relational

attributes such as inflexible, restrictive and ‘‘stuck in a rut.’’ Innovation is

expressed by terms such as open to new experiences, changeable and so on.

We can represent these considerations in the form of a module (or

diagram). In this way, it is possible to analytically deduce four basic modes

of experiencing and dealing with intergenerational ambivalences. Referring

to empirical findings and their discussion, as well as to conceptual consid-

erations, we went through different phases of representation.6 We also took

into account criticisms that representation in the form of a circumplex-model

suggests a static typology, in other words, one where a certain way of

dealing with ambivalences is viewed as finite. Overcoming this limitation

is highly desirable in the field of life course studies. It seems likely that

individual modes of experiencing ambivalences and coping with them change

as people move through different contexts and segments of their lives.

In order to visualize the dynamics of development e.g. the possibility to

move from one type of experience and of coping to another, we suggest

using the geometric form of a spiral. As for characterizations of the modes

of ambivalence, the already-existing descriptions seem still useful. Thus, the

modified module (graphic representation) can be presented and commented

on in the following way (see also Luscher & Pajung-Bilger, 1998; Luscher &

Lettke, 2002, 2004; as well as Lang, 2004; Brannen, 2003):

1. Solidarity refers to reliable support, or the willingness of the generations

to provide each other with services of a not necessarily reimbursable sort.

This involves the exercise of authority, but not in the sense of a one-sided

exertion of influence and power. Rather, it is understood as represent-

ative action including empathy. The maxim of action can be character-

ized as to ‘‘preserve consensually.’’ The members of a family feel

committed to their traditions and get along with one another quite well.

Thus, ‘‘solidarity’’ is one possible mode of dealing with intergenerational

ambivalences, which in this case may be more covert than overt. (It

should be noted that this term implies a specific notion of solidarity and

that the term ‘‘loyalty’’ may also be appropriate for this dynamic.)

2. Where family members strive for emancipation, actions predominate that

support mutual emotional attachment (convergence) and openness to-

ward institutional change (innovation). Relationships between parents

and children are organized in such a way that the individual development

and personal unfolding of all family members is furthered without losing

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sight of their mutual interdependence. This general setting contains a

certain amount of direct, common purpose pursued by efforts to ‘‘mature

reciprocally.’’ Tensions can be discussed openly, and temporary practical

solutions can be continually negotiated.

3. Atomization takes into account that family cohesiveness is no longer as-

sured by institutional ties and the subjective experiences of relational

histories. The concept expresses the fragmentation of the family unit into

its smallest components, specifically individual family members who

‘‘separate conflictingly.’’ Apart from the unalterable fact that family

members are parents and children, they otherwise have very little in

common. Actions follow a line of conflicting separation, although an

awareness of generational bonds remains.

Subjective (personal) dimension: Convergence vs. Divergence

Institutional dimension: Reproduction vs. Innovation

Captivation Atomization

EmancipationSolidarity

To preserve

consensually

To mature

reciprocally

To conserve

reluctantly

To separate

conflictingly

Divergence

Reproduction Innovation

Convergence

Intergenerational Ambivalence: A research module


4. Captivation designates cases where the family as an institution is invoked

to support the claims of one family member against another. A fragile

relationship of subordination and superiority thereby arises in which

moral claims and moral pressure are used to exert power. Usually one

generation, predominantly the parental, attempts – by invoking the in-

stitutional order – to assert claims on the other or to bind them by means

of moral appeals without, however, basing its claims on a sense of per-

sonal solidarity. The guiding maxim here is to ‘‘conserve reluctantly,’’

whereby family members may try to ‘‘instrumentalize’’ each other, not

respecting each other as subjects, but using each other as ‘‘means to an

end’’ or as objects.

I would like to underscore the heuristic character of the module. It is used

in an attempt to synthesize and visualize certain basic assumptions about

intergenerational ambivalence and to suggest a first set of labels for the poles

that characterize the dimension of simultaneously experienced juxta-

positions. It also suggests ways to see how the micro- and macro-systems

are embedded in a social ecology of action. The module, so far, emphasizes

the experience of ambivalences in relationships. Metaphorically, we can

evoke the image of a ‘‘dialogue with significant others.’’. Along this line, we

can think of other modi. Thus, we can comprehend the experience of am-

bivalences in the form of a ‘‘dialogue with oneself,’’ and furthermore as a

‘‘dialogue with generalized others,’’ namely as a quarrel with general nor-

mative (societal) expectations, or prescriptions.

As a general schematic representation, the module encourages further

differentiations and adaptations to specific research topics. Such specifica-

tions seem to be necessary, especially in applications to life course analysis.

Thus, I offer the foregoing conceptual ideas as a proposal to analytically

structure the field of research in terms of the concept of ambivalence, par-

ticularly in studying intergenerational relationships. Existing studies can be

characterized by the way, and to the extent that, they refer to elements of

this conceptualization, or use alternatives. The conceptualization represents

one of several possible approaches.

4. CURRENT STATUS OF RESEARCH

4.1. Methodological Preliminaries

Although this is not the place for a detailed methodological discussion (for

this see Lettke & Klein, 2004 and the literature discussed there), I will start

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with a brief comment on the possibilities to assess the experience of am-

bivalences and respective actions. In general, it seems more reasonable to

use qualitative methods. But we should not ignore the fact that they require

highly elaborate interpretative strategies in order to achieve inter-subjective

validity, especially when studying accounts given in everyday language and

experiences that are not always conscious. Beside the well-established re-

search techniques in the social sciences, advances may also be possible

through cooperation with literature studies. For instance, Zima (2002) pro-

vides a complex demonstration of ambivalence on the level of syntax, on one

hand, and on the level of semantics and content, on the other. In quan-

titative research, a major obstacle lies in the general orientation of many

scaling techniques, insofar as they strive for clarity, in an effort to strictly

avoid contradictions. In the available research on ambivalence, the follow-

ing approaches, techniques and methods are found:

1. Interview techniques addressing the awareness of ambivalence: Respond-

ents can be asked about their awareness of ambivalences in a more or less

direct way, by using the term itself or by presenting circumscriptions such

as ‘‘feeling torn in two directions.’’

2. Assessment of relationships with regard to covert ambivalence: Subjects can

be invited to characterize their relationships with polarized attributes pre-

sented separately, such as warm or loving for convergence, indifferent or

superficial for divergence. If the answers are contradictory, because both of

the two opposing attributes are simultaneously judged applicable, they can

be transformed into indicators of ambivalence. Currently, the most widely

used procedure is one proposed by Thompson, Zanna, and Griffin (1995).

3. Use of vignettes: Subjects are presented with situations in which they have

to make ambivalent choices.

In the following overview, I concentrate on contents. It is not meant to be

comprehensive, but rather illustrative. Its focus is on findings and studies,

mostly of a quantitative nature, which highlight aspects that may be espe-

cially relevant for transfer from the analysis of intergenerational relations to

life course research. The systematization is not a strict one, insofar as some

studies obviously concern different topics.

4.2. Assessment and Differentiation of Ambivalences

Ambivalences, formulated in direct or circumscribed ways, are part of eve-

ryday life and are therefore commonplace experiences for men and women,


parents and (adult) children. This finding has frequently been confirmed.

For instance, an exploratory study by Pillemer and Suitor (2002, p. 609)

demonstrates, ‘‘that direct measures of ambivalence toward children can be

used effectively...and that ambivalent assessments of the relationship are

sufficiently widespread to be of scientific interest.’’ In another analysis of the

same data, concerning mothers’ general assessments of parent–child

relationships, Pillemer (2004, p. 128) concludes that the ‘‘data offer con-

vincing evidence that parental ambivalence regarding adult children is suf-

ficiently widespread to be of scientific interest.’’ Similar conclusions can be

drawn from studies by Connidis (2001), Jekeli (2002), Spangler (2002), and

Willson, Shuey, and Elder (2003) and others.

Coenen-Huther, Kellerhals, and von Allmen (1994) made a survey of the

relations among kin in a representative sample of families. They discovered

that a majority of relations, approximately 60%, were experienced and

judged positively. However, one third (36%) referred to ambivalences, and a

small minority (4%) judged their relationships negatively. More interest-

ingly, the intensity of dilemmas rose with the frequency of mutual help.

Ambivalent judgment that are considered important can be detected in

about half of the cases. The authors conclude: ‘‘Intensive solidarity is not

self-evident’’ (Coenen-Huther et al., 1994, p. 334). Reluctance is apparent,

especially in long-term relations.

The ongoing studies at Konstanz (Luscher & Lettke, 2004) confirm that if

one asks about them directly, using everyday expressions, experiences of

ambivalence turn out to be almost commonplace. A similar picture emerges

from data concerning the answers to contradictorily formulated statements

about relationships, such as, for instance, the following statement: ‘‘[Name

of other person] and I often get on each other’s nerves, but nevertheless we

feel very close and like each other very much.’’7

In addition, these studies yield a finding that is particularly relevant for

life course research: The experience of ambivalence is not judged, per se, as

negative. Of importance seems to be the level, the intensity and perhaps the

context of ambivalent experiences. In other words, dealing with ambiva-

lences may be understood as a challenge, hence in the context of the life

course as a ‘‘developmental task.’’ Here, a connection exists to the origins of

the concept and its elaboration in psychotherapy, where several authors see

the acceptance or the ‘‘tolerance of ambivalence’’ as a criterion of growth

and maturity and stipulate it as a goal of therapeutic efforts.

We also find the idea of an optimal level in the experience of ambivalence,

for example in a study by Mayer and Filipp (2004). This questionnaire study

explored middle-aged adults’ perceptions of their parents’ generativity and

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the interpersonal consequences of these perceptions. The subjects assessed

the typicality of behaviors indicating generativity for their mother or father

and evaluated the parent–child relationship on several measures (affection,

manifest and latent conflicts). Some of those relations were moderated by

adult children’s positive regard for parental advice. Affection was highest at

intermediate levels of perceived generativity, but was also linked with mod-

erate levels of manifest parent–child conflict. In the understanding of the

authors, these results ‘‘suggest to analyze effects of generativity under the

aspect of intergenerational ambivalence’’ (Mayer & Filipp, 2004, p. 166).8

The idea of an optimum level is useful to interpret nonlinear variations

and correlations as the expression of the interplay between contradictory

forces. Such a view encourages a secondary analysis of existing research.

Empirical research on kin networks shaping the life course suggests that the

effect of support networks on conjugal quality is curvilinear (Holman,

1981), i.e., extremely cohesive networks might be detrimental to conjugal

functioning. The interference model (Johnson & Milardo, 1984; Julien,

Markman, Leveille, Chartrand, & Begin, 1994) states that social networks

and conjugal relationships may actually compete. Developing relationships

create anxiety in social networks, because the time and energy devoted to

other relationships are thereby reduced. Thus, social network members may

try to hold or regain some influence on their ego by interfering with conjugal

relationships. In this perspective, strong networks may not buffer the effects

of conjugal conflict, but may actually increase them, because the emergence

of conjugal problems opens doors to further interference by network mem-

bers with a couple’s relationship. These examples also invite us to look at the

dynamics of conjugal relationships as a field of overt and covert ambivalent

feelings and behaviors.

In the Konstanz studies, as outlined in the conceptual part of this chapter,

we emphasize the analytical distinction between an institutional and a sub-

jective dimension of ambivalence. The data suggest evidence for the fruit-

fulness of this idea. In general, ambivalences on the institutional dimension

seem to be more pronounced than on the subjective dimension (Lettke &

Luscher, 2001, p. 527ff.). This is true for both parents and adult children, a

finding which suggests, in addition, that the so-called ‘‘generational stake’’

hypothesis is questionable with regard to ambivalences. Overall, then, am-

bivalent experiences seem commonplace, yet they differ in character. In

other words, the concept of ambivalence should be differentiated. This is an

idea that can be traced back to Bleuler, who distinguished ambivalences of

feelings, cognitions and volitions. Other authors also adopt this view in their

current work (see for instance, Lorenz-Meyer, 2004).


Brannen (2003), in a small-scale study of four-generation families, pro-

vides a typology of intergenerational relations with respect to the transmis-

sion of material assets, childcare and elder care, sociability, emotional

support and values. It examines two a fortiori conditions that are thought to

shape intergenerational relations: (a) occupational status continuity/mobil-

ity and (b) geographical proximity/mobility. Four types of intergenerational

relations are generated by this examination: traditional solidaristic; differ-

entiated; incorporation of difference; and reparation in estrangement. The

authors look at families holistically and draw on the concept of ambivalence

to describe the forces which encourage family members to preserve family

patterns and divisive forces that lead them to strike out on their own. It

shows how, whatever the type of intergenerational pattern, each genera-

tional unit seeks to make its own particular mark.

4.3. Diversification of Contexts

In the wider horizon of a comparative study, Fingerman and Hay (2004,

p. 145ff.) ‘‘revealed that parents and their offspring do seem to experience

greater ambivalence toward one another than they experience in many other

social ties.’’ However, other relationships are also considered ambivalent, in

particular ties to romantic partners and ties to siblings. The authors’dis-

cussion hints at another topic of interest in the possible application of the

ambivalence perspective to the study of the life course. Since nearly all the

romantic partners of adults older than 20 in the Fingerman and Hay study

were spouses or cohabiting partners, they hypothesize that ‘‘proximity may

play a role in the experience of ambivalence with romantic partners and

siblings. When siblings grow up and no longer live in the same household,

there is a precipitous drop in the likelihood that they will be classified as

ambivalent; teenagers classified their ties to siblings as ambivalent, whereas

individuals in their 20 s did not. It may simply be the case that individuals

are more likely to experience ambivalence when they occupy the same life

space. This pattern regarding proximity was not the same for parents and

children, however. Adult children in their 20 s who do not reside in their

parents’ households were more likely to consider their ties to their parents

ambivalent than were teenagers who lived with their parents. Therefore,

ambivalence between parents and children may reflect different factors than

does ambivalence in other social ties’’ (ibid.). The conclusion that suggests

itself is plausible: The experience of ambivalences may change over the life

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course, but this is certainly only the starting point for a range of propo-

sitions still to be developed.

In an extension and follow-up of the survey done at Konstanz (see above)

using as far as appropriate the same instruments, interviews have been made

of two types of families facing specific tasks and difficulties. In one group,

an adult child suffers from schizophrenia, in the other group, an adult child

is on drugs. In both instances, the child was living in a clinical institution at

the time of the research. This design allows, among others, a comparison

between statements concerning the relationship to the sick child and to other

children in the same family. The data show, as hypothesized, a higher fre-

quency of ambivalence in the relationship with the sick child, and a lower

relationship quality. Surprisingly enough, there is no significant difference in

feelings of connectedness to the children in the families (Brand, 2004;

Rudorf, 2004; Burkhardt, 2005).

Taking into account additional findings, the general conclusion for this

study is the conclusion is justifiable that most parents distinguish among

their children in many ways, yet they feel close to and committed to all of

them. These results give rise to certain doubts and criticisms of the holistic

view of families propagated by some popular systemic approaches used in

family therapy. More generally, we may again observe that the usage of

indicators of ambivalence, i.e. the ambivalence perspective, promises an

understanding of families that reflects their internal dynamics and therefore

comes close to real life. The subjective attitudes and orientations of family

members are taken into account without neglecting the role of institution-

alized bonds.

The concern for parent–child relationships in exceptional families is also

reflected in studies of families with gay or lesbian children. A large body of

research is available; Cohler (2004) offers a comprehensive overview draw-

ing upon the interpretative power of the concept of ambivalence. Among the

many topics covered, of particular interest in the life course perspective is

the process of ‘‘coming out.’’ It is subject to several forms of ambivalence

and requires different strategies of coping, e.g. with regard to personal

sameness and difference, to traditional and new life styles. Parents may also

have the task of revealing their child’s sexual orientation to kin and friends.

On another level, a kind of institutional ambivalence may be implied in

the way legislation deals with homosexual partnerships. Should they be

treated as just another form of marriage, or should a special legal institution

be created (e.g. civil partnerships or civil unions, as is the case in most

European countries)? Do gays and lesbians themselves want to accept rules

derived from traditional marriage, especially with regard to the dissolution


of the relationship? Quite to the point, the German author Lautmann (1996)

uses the notion of ‘‘ambivalences of the law.’’

Extending the horizon, it is easy to propose other family configurations as

breeding grounds for latent and manifest ambivalences. In single-parent

families, relationships with the absent father or mother and struggles for

custody may bear all the features of an enduring conflict, putting the child in

an ambivalent position. In the case of foster families, the child as well as

those who have institutional responsibilities for the arrangement, such as

social workers, may find themselves caught up in struggles between the

biological mother and the so-called social parents (or legal parents). Here

too, legal regulations and procedures may be relevant to the search for a

way of pragmatically coping with ambivalences. In Germany, this is the case

for the legal obligations of adult children to support their parents when they

are poor and need institutional care (Hoch & Luscher, 2002).

Divorce at all stages of marital and generational biographies may accen-

tuate, often over a longer period or for an entire lifetime, overt and covert

ambivalences. One is reminded of the proposal by Cherlin (1981) to view re-

marriage as an incomplete institution. In these and comparable cases a

specific and elaborate operationalization of the concept of ambivalence is

needed if one wants to go beyond simple plausibility. As a result of these

efforts, one can expect, as mentioned above, at least a higher level of au-

thenticity with regard to the diversities and the dramas of everyday life. One

should also strive for findings, which systematically illuminate the conse-

quences of different levels of awareness and of different strategies in dealing

with ambivalences. Practical interests may lie in the evaluation of thera-

peutic interventions that strive to heighten the awareness of ambivalences

and to establish specific ways of dealing with them.

4.4. Ambivalences at Turning Points and Transitions

The notion of turning points refers to phenomena, experiences and actions

where the awareness of ambivalences may be especially promising and

where the interplay between generations and the life course is quite perti-

nent. A turning point may be understood, metaphorically speaking, as an

interruption in a person’s development. It coincides with the necessity, or at

least the possibility, to reflect upon personal relationships and the commit-

ments they involve. Changes may be requested and importance attributed to

particular relationships, or persons may be asked to restructure their rela-

tionships. New commitments and obligations may emerge that compete with

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ongoing concerns and ties. In reality, ‘‘turning points’’ may extend a certain

period of orientation and search, hence it is also appropriate to speak of

transitions. They can be seen as fields of action entailing an accentuated

experience of ambivalences.

Perhaps the most obvious turning point at the intersection between in-

tergenerational relationships, the life course and the social context is the

transition to parenthood. This appears in many ways and, not surprisingly,

there is still no comprehensive theory of generative behavior and decision-

making. Several attempts, however, refer to the notion of ambivalence,

mostly using the word in an everyday meaning. More elaborate studies

along this line point out that decisions are reached only through a lengthy

process that takes the form of oscillations typical of ambivalences. A good

illustration is the phenomenon of late first motherhood (see Engstler &

Luscher, 1991).

The experience of ambivalences (as defined above) is bound to the self and

personal identity. In addition to their search for the subjective meaning of

motherhood, many women are confronted with or exposed to normative

expectations, traditional or progressive, by others who are close to them,

and also by society at large, as represented by subcultures such as religions

and ethnic groups, not to speak of economic pressures and the contempo-

rary organization of the labor market. This topic also illustrates what is

referred to above as the experience of ambivalence ‘‘in the dialogue with

generalized others.’’

An attempt to draw upon the concept of ambivalence and to further

explore its relevance for a typological differentiation of generative behaviors

is offered as part of in an analysis of the Swiss Family Survey (Le Goff,

Sauvain-Dugerdil, Rossier, & Coenen-Huther, 2005). Ambivalence is used

as an alternative to the notion of rational choice in discussing fertility be-

havior in low-fertility countries like Switzerland. It serves as a key concept

to distinguish between four main types of the fertility project: The familialist

subculture, either sequential or simultaneous articulation between labor

market participation and motherhood, and childlessness. Future trends are

discussed in the light of the pressure to change exerted by those women who

experience a high degree of ambivalence between their own life aspirations

and normative expectations, while also possessing high levels of personal

resources.

With regard to motherhood as such, a treatise by Parker, with the sug-

gestive title ‘‘Mother Love, Mother Hate,’’ written from a psychoanalytical

perspective, merits special attention. Parker (1995, p. 6) refers to Melanie

Klein, who ‘‘considered that ambivalence had a positive part to play in


mental life as a safeguard against hate.’’ Parker adds: ‘‘I want to go further

and claim a specifically creative role for manageable maternal ambivalence.

I suggest that it is in the very anguish of maternal ambivalence itself that a

fruitfulness for mothers and children resides.’’ The major mechanism can be

described as follows: Given the fundamental dichotomy and the awareness

of love and hate, mothers are able even in desperate situations to reactivate

the forces of love. More generally, mothers search continuously, even under

difficult situations, for arrangements that serve the well-being of their chil-

dren. This fundamental ability to cope with ambivalence creatively can be

seen as a genuine cultural and social contribution of mothers to civilization.

Contributions like Parker’s make clear why – and also how – a focus on

ambivalence can be compatible with feminist thinking. This field is sensi-

tized to possible ambivalences in gender relations and to constructive, as

well as destructive, strategies for dealing with them.

Referring to a later phase in the life course, Pillemer and Suitor (2002)

focus on the tension between autonomy and dependence and find that a key

dilemma leading to intergenerational ambivalence is the conflict between the

norm of solidarity with children and the normative expectation that children

will develop independent lives in the case of the so-called ‘‘off-time tran-

sitions’’ – here in the lives of children. As a general finding, the authors

showed, ‘‘that adult children’s failure to achieve and maintain normative

adult statuses and financial independence, and mothers’ developmental

stage predict ambivalent assessments of the relationship. Regression anal-

yses supported these hypotheses and also revealed that the variables pre-

dicting ambivalence differed from those that predicted closeness and

interpersonal stress’’ (Pillemer & Suitor, 2002, p. 602). In particular, height-

ened ambivalence can be anticipated when adult children have not attained

(or maintained) adult statuses. When parents face such unexpected circum-

stances, they are likely to experience mixed emotions involving a desire to

protect and assist the child, as well as disappointment at the child’s situation

and self-doubt regarding parenting. This study, like the one mentioned be-

fore, makes explicit use of the concept of ambivalence. It is not difficult to

imagine other turning points that display preconditions for the experience of

ambivalences, such as occupational choice, or – at the end of a professional

career – the period of retirement. Work in these areas would require – and

could stimulate – further efforts in the conceptualization of ambivalence.

The example suggests viewing non-normative (or even deviant) behavior

as a cause of ambivalence. From a theoretical point of view, there may be a

linkage with the analysis of stigma, such as that of Goffman (1963). In-

terestingly enough, although the latter does not use the term ambivalence,

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he describes behaviors that can be interpreted as strategies for coping with

ambivalences.

If attention is directed toward specific features of the life course, trauma is

certainly an experience that can generate ambivalences in several ways. Under

the impact of personal and structural violence, the self is threatened, and this

may remain so for a long time, or even lifelong. Thus, the traumatic expe-

rience becomes part of the personality. On one hand, it is so subjective that it

cannot be shared with others, but on the other hand there may be a strong

desire to share one’s experiences, not least of all in the hope of receiving

therapeutic support. This holds true for personal traumatic experiences such

as child abuse. Traumas can also be collective, as in the case of wars. The

Holocaust is a unique case of the experience of collective trauma for which an

extensive body of literature exists (see for example Ludewig-Kedmi, 2001,

2004). The twofold experience of ambivalence in connection with personal

and collective trauma is concisely summarized by Smelser (2004, p. 53) in the

following passage:

One of the peculiarities that have been noticed in connection with acute psychological

traumas is a very strong dual tendency: to avoid and to reliveyAt the ideational level

one main defense is some form of amnesia (numbing, emotional paralysis)y, actual

forgetting, denial, difficulty in recalling, or unwillingness to contemplate or dwell on the

traumatic event. At the same time, the trauma has a way of intruding itself into the mind,

in the form of unwanted thoughts, nightmares and flashbacks. These apparently an-

tagonistic tendencies have presented themselves to some as a paradoxy At the behavi-

oral level, the same double tendency has been observed: A compulsive tendency to avoid

situations that resemble the traumatic scene or remind the victim of it, but at the same

time an equally strong compulsion to repeat the trauma or to relive some aspect of ity

When seeking an analogy at the socio-cultural level, we discover such dual tendencies –

mass forgetting and collective campaigns on the part of groups to downplay or ‘put

behind us’, if not actually to deny a cultural trauma on the one hand, and a compulsive

preoccupation with the event, as well as group efforts to keep it in the public con-

sciousness as a reminder that ‘we must remember’, or ‘lest we forget’, on the other.

A memorial to an eventyhas both reactionsy[we can speak of] the compulsion to

remember and the compulsion to forget.

4.5. Ambivalences Concerning Specific Fields of Action

4.5.1. Caring

The experience of ambivalences may be greater in tasks where tensions and

contradictions cumulate. This is certainly the case in caring. For caregivers,

and in reference to the subjective component of relationships, sympathy and

antipathy are at play, and many caring activities include intimate behaviors


that may be embarrassing. From an institutional perspective, normative

expectations may exist which juxtapose the commitment of a woman as the

daughter of elderly parents with the duties of husbands and wives. Men, too,

may be burdened in this way, but caring is still considered a primarily female

obligation. These traditional gender ideologies may add to the pressures and

thereby further the likelihood of ambivalences. Seen from the point of view

of the care-receivers, ambivalent feelings and attitudes may exist as well,

since they realize the tensions between insight into apparent dependency and

the wish for independency.

This is the topic of a monograph by Cohler and Grunebaum (1981) which

is cited here as an example of a study that analyzes the phenomenon of

ambivalence without using the concept itself. The authors focus on mother–

adult daughter relationships in four families of Italian Americans. Their

point of departure is the ‘‘paradox in contemporary society where, on the

one hand, it is believed that adults will strive to become both psychologically

and economically autonomous and self-reliant, while, on the other, findings

from systematic investigations of family life show that dependence across

the generations is the typical mode of intergenerational relations, including

the interdependence of very old parents on their middle-aged offspring’’

(ibid., p. 10). In the concrete case, for the mothers, the acceptance of the

daughters commitments are in conflict with the mothers desire to continue

to lead their own lives. The authors describe as an illustrative example the

relationships of one mother (Mrs. Scardoni) and her daughter (Mrs. Russo)

in the following way:

Mrs. Russo’s continuing emotional involvement with her mother is both a source of

support as well as a source of considerable discomfort and strain. Neither Mrs. Scardoni

nor Mrs. Russo can tolerate any disagreement or disharmony, for neither mother nor

daughter can admit to their own mixed feelings. On the one hand, Mrs. Russo is very

dependent on her mother for help with even the most minute aspects of her life, such as

recipes for supper or advice on her problems with her daughter or her husband. On the

other hand, she is afraid that her mother will forget about her if she does not maintain

continual contact. Burdened by her mother’s demand that she and her brother provide

Mrs. Scardoni with the identity that she had never achieved for herself and unable to

derive any sense of security or satisfaction from their relationship, Mrs. Russo feels

frustrated, resentful, and then guilty. Finally, she becomes so distraught that she can

only continue to function by swallowing large doses of the several ‘tranquilizers’ that her

family doctor has prescribed for her. (ibid., p. 120)

A similar study of fathers and sons in later life has been published by

Nydegger and Mitteness (1991). Their analysis also contains colorful de-

scriptions of ambivalences without using the term itself. There are certainly

more studies which contain an implicit and consequently not yet elaborated

KURT LUSCHER114

reference to the idea of ambivalence. It may be worthwhile to reanalyze

them in the light of the emergence of a theory of ambivalences.

In this regard, a secondary analysis of data from the Berlin Aging Study

(BASE) by Lang (2004) merits special attention. It also contains an explicit

connection to the life course approach. Data are available from responses by

adult children (mean age 54.4 years) to a mailed questionnaire on personal

networks and the quality of relationships with parents. Ultimately,

yfour distinct patterns of adult children’s relationship styles towards their parents were

identified based on indicators of support exchange, personal norms and affective

strength: close exchange, resilient giving, strained altruism, and detached distance. The

four relationship styles were associated with motivations for seeking contact with par-

ents and the inconsistency of relationship satisfaction with parents. Each of the four

relationship styles reflects an individual response to the challenges of the filial task in

midlife.

In the interpretation of the author (Lang, 2004, p. 199ff.),

yfour observed styles of adult children’s relationships with their older parents are most

consistent with the assumptions of the heuristic model of intergenerational ambivalence

(Luscher, 1998). According to this model, ambivalence is conceived as an implicit and

underlying structure that may be experienced within any intergenerational relationship.

For example, adult children may respond to ambivalence with detachment from their

parents, referred to as atomization. This response is well reflected in the detached-distant

relationship style of adult children’s attitudes towards their parents. Another prototyp-

ical response described in the heuristic model of ambivalence is captivation, which refers

to feelings of being obligated to take responsibility, while at the same time feeling

strained by such responsibility. This response pattern is well reflected in the strained-

altruistic relationship style of adult children. A third prototypical response to inter-

generational ambivalence according to the heuristic model of ambivalence is the

expression of normatively taking responsibility and close supportive exchanges with the

aged parent. This response pattern may be characterized as solidarity and is best re-

flected in the group of adult children who display a style of close exchange with their

parents characterized by strong emotional closeness and much supportive exchange with

parents. The relationship style of close exchange with parents comes closest to the

concept of family solidarity, at least with respect to the constructs of normative, func-

tional and affective solidarity. Adult children in this group were mostly satisfied with

their relationship to their parents and displayed the strongest level of consistency across

different ratings of satisfaction. A fourth prototypical response pattern refers to eman-

cipation, which involves a pragmatic attitude of keeping an affective distance to one’s

parent while at the same time giving what is needed. Again, this response pattern is

reflected in the group of adult children who displayed a style of resilient giving towards

their parents. Adult children of this group gave much support because they felt obliged

to do so, but also showed relative affective neutrality towards their parents.... Mani-

festations of personal ambivalence as indicated by the degree of inconsistency in ratings

of satisfaction with parents were differently distributed across the four relationship

styles. In particular, the strained-altruistic and the resilient-giving relationship styles


were found to have the greatest potential for perceptions of ambivalence (i.e. incon-

sistency). Both styles were associated with a basic and strong attitude towards giving

support to one’s parents.

Lorenz-Meyer (2004) has explored, through narratives of young adults in

Germany, the generation of ambivalences and strategies of dealing with

them in relation to prospective parental care. In her own words, ‘‘the anal-

ysis shows that in contemporary Germany the (anticipated) transition of

parents requiring personal care is perceived as a structurally ambivalent

situation for many adult children that simultaneously values two opposing

courses of actions and leads to decisional ambivalence of children between

personally supporting their parents in old age and placing them in a nursing

home. Participants’ reflections on viable and consensual care arrangements

that can be interpreted as an attempt to deal with decisional ambivalence

involved a multifaceted process of taking stock of (a) the personal

relationship between parents and children, often in comparison with the

relationship between parents and siblings; (b) the living situation of older

parents; (c) the respondent’s own living situation; (d) past family care ar-

rangements; (e) cultural-normative guidelines; (f) care institutions; and

(g) expected commitments of other siblings (and partners).’’ (ibid., p. 246f).

The interviews also show, ‘‘that research participants interpreted ambiva-

lences not just in a biographical, but also in a socio-historic context. Par-

ticipants’ localization of intergenerational positions and relationships in

concrete historical conditions can serve to de-personalize and possibly mit-

igate personal ambivalences’’ (ibid., p. 248).

In the context her analysis, Lorenz-Meyer also focused on points of con-

nection and differentiation with the four strategies of dealing with ambiv-

alence identified in the Konstanz studies. ‘‘Displaying inaction and not

planning for parental care needs, for example, was not considered as con-

tradicting a solidaristic orientation (and could even be interpreted

as ‘‘emancipation’’ in the Konstanz typology, if previous familial care

arrangements were not reproduced and personal contact maintained).

This was a strategy of dealing with ambivalence that was used mainly by

men. The assumption that other siblings, usually a sister, would provide

co-residential care tended to facilitate inaction and mitigate decisional am-

bivalence. Conversely, it was exclusively women (with intermittent employ-

ment) who committed themselves to providing co-residential care (that can

be interpreted as ‘‘solidarity’’ or, if the initiation of alternative arrangements

had failed, as ‘‘captivation’’). Women were also the majority of those who

KURT LUSCHER116

explicitly anticipated accommodating the parent in a home while commit-

ting themselves to complementary emotional care (which can be interpreted

as ‘‘emancipation’’ if elder care had been provided in the family). For both

groups of women the perceived absence of care commitments from other

siblings increased decisional ambivalence. A crucial factor for planning res-

idential (rather than co-residential) care was the availability of material

resources to afford quality care among women (and some men) with more

continuous employment that thereby had a mitigating effect on decisional

ambivalence.’’ (ibid., p. 249).

Lorenz-Meyer distinguishes between multiple, personal and structural

ambivalences that underlie decisional ambivalence in the following way:

� Personal ambivalences refer to the simultaneity of opposing feelings

and orientations such as closeness and distance that came to the fore

when participants imagined co-residential living arrangements with their

parents.� Structural ambivalences refer to the simultaneity of opposing offerings,

directives or guidelines for action inherent in institutional structures, such

as state agencies or social policies.� The notion of multiple ambivalences refers to overlapping personal and

structural ambivalences that constitute multiple sources, rather than a

single cause for decisional ambivalence.

As part of the already-mentioned OASIS project, an extensive compar-

ative study on the care of the elderly and the role of family support systems,

complemented the traditional focus on solidarity with an analysis of am-

bivalences. The authors summarize the results of the quantitative and the

qualitative analysis as follows (Lowenstein & Ogg, 2003, p. 223):

Correspondence analysis of the ten questions relevant to inter-generational conflict,

ambivalence and solidarity resulted in categorizing parent–child relationships into four

distinct styles. Harmonious relationship styles were categorized, for example, by getting

along extremely well but with an acceptance that conflict and ambivalent feelings could

and did occur but without altering the essentially positive relationship experience. Dis-

tant family styles were conversely evidenced by emotional distancing, differences in view

and the experience of conflict and ambivalent feelings in a way which could or did have a

deleterious effect on family relationships. – In the qualitative data, dyads who expe-

rienced their relationships as effective and essentially harmonious tended to identify

ambivalence or conflict as a part of the process of their relationship. Transitions created

by changes in parental health for example, brought about the possibility of negotiating

or redefining roles and responsibilities without impinging on participants’ views of the

overall quality of the relationship.


4.5.2. Inheritance

If one is searching for phenomena that seem in the light of experience to be

breeding grounds for ambivalences, inheritance is undoubtedly a major

candidate. Thus, we may use this topic as an illustration of how the new

orientation, namely the interest in ambivalences, sheds light, encourages,

stimulates new research interests, close to daily life, and also recalls the

importance of interdisciplinary cooperation. Certainly a core phenomenon

in the field of generations, inheritance has found surprisingly little attention

in the field of social science. This is also true for its relevance for patterns of

life courses, individual lives and personal ties.

The chapter by Plakans (2004) in Pillemer and Luscher (2004) is a good

starting point. The author recalls how important the regulations concerning

inheritance were in the past and how much they could influence the life

courses of the rich, including aristocrats, as well as peasants and artisans.

Major sources of ambivalences can be assumed on a structural level in the

juxtaposition of institutional rules and customs, and the desire of the dona-

tors to express their personal sympathies, or to reward a child (or another

person) for support and attention. Another conflict which most likely in-

duced everyday ambivalences has to be seen in the self-interest of the old in

their role as heads of households, as opposed to the desire of the young to

have a family of their own and to become autonomous. Ambivalences may

also be nourished by the rivalries among siblings.

To this Plakans offers concrete illustrations. Ehmer and Gutschner (2000)

confirm the overall fruitfulness of the concept of ambivalence for the study

of inheritance and more generally speaking for the social history of the

family and its implications for personal biographies. They see a major ad-

vantage or function of the concept in that it serves to deconstruct the ide-

alizations that have long dominated family rhetoric.

An attempt to include the concept of ambivalence in a study of present-

day processes of inheritance has been made by Lettke in the Konstanz

Inheritance Survey (Konstanzer Erbschafts Survey – KES), which is rep-

resentative of the German population age 40 and above, using the method

of telephone interviewing. His findings confirm, that about a third of the

subjects refer to ambivalences – a number which seems lower than one

would expect at first glance, and with regard to the usual socio-demographic

variables, those with lower levels of education show a significantly higher

rate of ambivalence. A more detailed analysis reveals that those who

have already received an inheritance are significantly more ambivalent,

which suggests that actual experience turns out to entail more difficulties

than anticipated. Strong correlations exist between the experience of

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ambivalences and the responses in terms of motivation. The following in-

stances appear to be of significant importance: the intention to reward those

who have provided care, who are especially sympathetic, by whom one

wants to be remembered and with whom one shares common convictions

and beliefs. Ambivalences also arise if a person wants to support children

who have a family of their own and those who are in need. More generally,

ambivalences seem to increase if the testator has reasons to deviate from the

rules stipulated by the law and by a general societal idea of equity. With

regard to the dimensions of the module suggested above, inheritance seems

to be a field of action where the tensions between the subjective or personal

and the institutional dimensions seem of particular relevance.

5. OUTLOOK

In this section, I offer some proposals for a greater rapprochement between

the study of intergenerational relationships and the study of the life course,

especially with regard to its institutional embeddedness. Such an orientation

refers back to the older issue of the interplay between biology and culture,

which is fundamental both to the concept of human development and

generational succession. A major focus is the understanding of personal

identity and the self.

In this connection, and also as an answer to recent calls for more there in

the field of generational studies, the concept of ambivalence is appropriate.

This is appropriate and attractive for at least three reasons. First, this con-

cept too is relevant for a deeper understanding of personal identity in a non-

metaphysical and non-normative way. Second, if used in the sense of Mead

(1938), identity development can be understood as advanced by ongoing

dialogues with oneself and with significant others. Third, such dialogues

imply the possible experiences of being torn in two opposed directions and

oscillating between them.

With regard to a life course perspective, ambivalences are presumed to

activate, or at least to stimulate, the human potential for action in social

structures. In other words, dealing with ambivalence requires ‘‘agency.’’

Thus, it is fruitful to view ambivalences as ‘‘neutral,’’ i.e. as possible pre-

conditions for acting. Research on ambivalence should therefore focus on

awareness and coping. We can hypothesize, first, that people cope with

ambivalence in more or less competent, productive, or even creative ways.

Second, the deliberate construction of ambivalences can be a strategy in

shaping and organizing social interactions. Third, the personal experience of


ambivalences depends on aspects of interactions and social structures

and on the embeddedness of ambivalences in role models and collective

identities.

We can expect that ambivalence will be especially manifest at ‘‘turning

points’’ and that it will likewise be apparent throughout the biographical

histories of the relationships between parents and their children. Ultimately,

dealing with ambivalences can be conceptualized as a ‘‘meta-task’’ of the

personal and social organization of intergenerational relations (and other

kinds of social relations) over the life course (and vice versa).

In addition, we may hypothesize (beyond the existing frameworks) that

ambivalences, in a life course perspective, may be experienced in introspec-

tion (‘‘inner dialogues’’), as suggested by the idea of ‘‘life review’’ or ‘‘life

reflection’’ (Staudinger, 2001). They may be experienced (and have to be

dealt with) in social relationships (‘‘dialogues with others’’). Finally, one

may consider the impact of generational politics (and politics in general) as

creating conditions that can generate ambivalences (‘‘dialogues with gen-

eralized others’’). Recalling the frame of reference presented at the PaVie

Colloquium Lausanne, we may ask where the experience of ambivalences

can be expected to occur and where we may discover specific strategies of

coping. I offer the following overview:

Topics Perspective of Subjects Perspective of

Researcher/Structures

Trajectory Life reviews Socio-biological

foundations

Conflict nature/nurture

Stage Reproductive behavior Stages of development

Transitions Leaving home Developmental tasks

Retirement Generativity (Erikson)

Events/tasks/

roles

Caring

Grandparenthood

Inheritance (multiple sense)

Trauma

Newly introduced concepts also engage us to adopt a new perspective in

examining existing theories and their interconnections. In this regard, fur-

ther explorations within the field of intergenerational relationships, as well

KURT LUSCHER120

as the field of life course analysis, may well be undertaken in regard, for

instance, to Erikson’s well-known theory of identity. His schema of eight

stages in the development of identity can certainly be read as a sequence of

dilemmas with ambivalent qualities. However, Erikson’s theory would have

to be linked systematically to descriptions of conduct, social relationships

and roles and their possible relevance for the emergence of ambivalence.

Another bridge can be built to the theory of generativity. In a recent, very

concise summary of its substance by McAdams and Logan (2004), at least

the second proposition points to a logical structure of the concept which

comes close to ambivalence: ‘‘Generativity may spring from desires that are

both selfless and selfish’’ (p. 18).

If we want to strive for a closer integration, we should be aware that the

focus here has been on relationships. This focus may be welcome in studies

of the life course. The linkages between lives merit greater attention. Quite

obviously, this draws attention to the dynamics of interpersonal relation-

ships. The idea of ambivalence, as obvious as it may be in the case of

intergenerational relationships, can certainly be enlightening for other per-

sonal relationships, such those between partners or husband and wives,

siblings and even friends and comrades. Their dynamics over a life course

may be quite meaningful and consequential.

Despite the importance of the concept in relationships, a self-critical ob-

servation may well be appropriate. At the present stage of the development

of the ambivalence perspective, concern with the consequences of ambiv-

alent experiences is unexplored. The distinction of four different modes in

dealing with ambivalences may well be a first step. Yet, more work is need-

ed. As one direction, I would like to offer the following argumentation. The

experience of ambivalences – it has been said – should be seen as relevant for

the development of the self or personal identity. (This connection is also

useful to distinguish ambivalences from trivial experiences of tensions and

choices in daily life). Within the framework of a theory of social action, the

reference to the self or personal identity implies a close connection to the

concept of agency, insofar as it may be understood as the locus of action and

of control (see also the chapter by Marshall, in this volume). Therefore, we

should pay greater attention, on one side, to the extent to which the pos-

sibility (or the inability) to control one’s own behavior with regard to others,

and therefore to shape relationships, is a source of ambivalences, and in

what ways the mastering of ambivalences goes together with the exercise of

power. Preliminary considerations along this line have been presented

by Connidis and McMullin (2002). Other behavioral consequences of

dealing with ambivalences may be considered as well. In short, a closer


interconnection between the study of ambivalences, agency and social con-

trol seems desirable and promising.

I would like to finish on a more general note by referring to Smelser (1998,

p. 13). Exploring the deeper meanings of the ambivalent, he states that, ‘‘we

are dealing with a fundamental existential dilemma in the human condition.

It is communicated in various dichotomies – freedom versus constraint,

independence versus dependence, autonomy versus dependence, maturity

versus infancy, and more – but ever the dichotomy, the dilemma appears to

be insoluble.’’ In a time, when professional and even economic interests play

a major role in the enterprise called ‘‘social science,’’ a reminder of some

basic humanistic issues may well be appropriate – not least with regard to

the question of how we organize and can organize our lives over the life

course and master – as chances and as burdens – the ambivalences occurring

in social relationships.

NOTES

1. Note the deliberate formulation as a general heuristic hypothesis: It is notsuggested that intergenerational relationships are per se ambivalent, or that theyalways require dealing with ambivalences, but several reasons and observations, asshown below, speak for the assumption that this may often be the case.2. From a systematic point of view, this reference to literature implies an impor-

tant insight: Insofar as fictional works are or can be seen as constructions of im-agined worlds, one also may see the ambivalences as deliberately constructed. Thismay be done on the assumption that these ambivalences are also experienced byreaders or viewers in their personal lives. The deliberate creation of ambivalences isalso used as a technique in certain psychotherapeutic methods. e.g the so-called‘‘paradoxical intervention’’3. For a more detailed, yet still preliminary overview see Luscher, 2004. Some

references to the role of the concept in different discourses can be found in Smelser(1998). The reception of the concept in psychotherapy is outlined in Otscheret (1988)and Knellessen (1978).4. Thus, we could also say that the concept of ambivalence refers to ‘‘decision-

making as a process’’ for which the metaphor of ‘‘oscillation’’ seems quite appro-priate, or as an alternative image, a ‘‘tug-of-war.’’5. Due to spatial limitations, I will focus only on the broad outlines. For a full

presentation, see Luscher & Lettke, 2004. The research instruments developedat Konstanz, in partial cooperation with Karl Pillemer, are available in English andGerman under: http://www.uni-konstanz.de/FuF/SozWiss/fg-soz/ag-fam/famsoz-i.html6. Here, a note about the methodological status of a diagram may be in order.

Following an idea by Bogen & Thurlemann (2003), diagrams represent a uniquecategory of ‘‘text,’’ which stems from the combination of words and graphics. Due toa certain degree of ambiguity and of openness, this kind of representation encouragesfurther interpretations and can thus serve as a means to develop new ideas and evenhypotheses.

KURT LUSCHER122

7. This example is taken from the questionnaires used in the Konstanz studies, seefootnote 5.8. There is a parallel to the finding of Pyke and Bengston (1996). In a qualitative

research project on family elder-care, they coined the concept of ‘‘overcare,’’ definedas care exceeding recipients’ actual needs which thus may have negative conse-quences, both relational and developmental. Close-knit networks may not alwaysfacilitate parent–child relationships, especially when the expectations of parents andother network members about the child are inconsistent (Belsky, 1984), or whennetwork members are perceived by parents as competitors rather than as supportersin the parenting process (Robertson, Elder, Skinner, & Conger, 1991). It might wellbe worthwhile to reanalyze these studies in the light of the emerging theory ofintergenerational ambivalence.

ACKNOWLEDGMENT

I would like to thank James Stuart Brice for support in stylistic and editorial

matters, and Denise Ruttinger for general assistance.

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KURT LUSCHER128

PART II:

TRANSITIONS

129


130

AGENCY AND STRUCTURE IN

EDUCATIONAL ATTAINMENT

AND THE TRANSITION

TO ADULTHOOD$

Jeylan T. Mortimer, Jeremy Staff and Jennifer C. Lee

INTRODUCTION

A macrostructural perspective highlights cultural values, normative timeta-

bles, stratification processes, and institutional career lines as determinants of

the content and pacing of role enactment through the life course, and the

acquisition of corresponding role identities. A social psychological perspec-

tive, in contrast, emphasizes the exercise of human agency as a central causal

force in shaping the life course, including the expression of values and

identities, self-regulative processes, decision-making, and striving to achieve

personal objectives through goal selection, strategic planning and action.

According to the macrostructural perspective, the life course consists of

sets of interrelated trajectories (of education, work, family, community

participation, and so forth) with an individual’s location in each at any

$This research was supported by a grant ‘‘Work Experience and Mental Health: A Panel Study

of Youth,’’ from the National Institute of Chile Health and Human Development (HD44138)

and the National Institute of Mental Health (MH42843).





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10004-5

131

given stage heavily dependent on positioning in prior phases. For example,

in the US context the socioeconomic status of the family of origin affects

children’s schooling in the very early years (placement in reading groups,

marks received, grade retention), which in turn influences assignment to

middle school and high school ‘‘tracks,’’ the opportunity to take college

preparatory and ‘‘advance placement’’ courses, and consequently, the like-

lihood of entering a selective college, or even any institution of higher ed-

ucation (Entwisle, Alexander, & Olson, 2003). The educational trajectory is,

in turn, closely linked to the trajectory of occupational achievement.

A macrostructural perspective strongly informs sociological models of

intergenerational occupational mobility. In these models, social psycholog-

ical processes involving significant others’ influence and aspirations are

considered essentially as intervening variables, mediating the relations be-

tween socioeconomic origins and destinations. At the extreme, expressed

aspirations and goals for the future may be interpreted as signifying mere

recognition of likely outcomes, given initial starting points, rather than

psychological constructs that have their own motivating and determining

force. The psychological constructs may predict subsequent outcomes, but

for structural reasons alone.

The second social psychological approach, while acknowledging that so-

cial location yields diverse and unequal opportunities and constraints at all

stages of the life course, emphasizes the differential capacities of individuals

to effectively exercise agency or initiative. Important individual differences

enable some to take fuller advantage than others of the opportunities linked

to their structural positions, or to more effectively overcome the constraints

associated with disadvantage. Social scientists who take this point of view

focus on psychological processes that lead to the crystallization of goal

formation and channel individual action in preferred directions. Gecas

(1991) argues that the self-concept itself is a motivating force because per-

sons strive to protect and to enhance their self-esteem, sense of efficacy, and

authenticity, and to validate their valued character identities. Individuals

express their identities most notably through role selection and perform-

ance. Personal constructs that implicate future action, such as ‘‘the possible

self’’ (Markus & Nurius, 1986), ‘‘self-efficacy’’ (Bandura, 1997), ‘‘planful

competence’’ (Clausen, 1993), ‘‘locus of control’’ (Rotter, 1966), and ‘‘au-

thenticity’’ (Gecas, 1991) are of central interest, since these are thought to

guide strategic action. Processes of ‘‘goal selection,’’ ‘‘compensation,’’ ‘‘op-

timization,’’ ‘‘assimilation,’’ ‘‘accommodation,’’ planning, and related proc-

esses (see Wiese, Freund, & Baltes, 2000; Brandstaedter, 1998; Heckhausen,

1999) may be more or less adequate in bringing about the more favorable

JEYLAN T. MORTIMER ET AL.132

life course outcomes. Individual orientations and actions are presumed to

have significant consequences for life course trajectories of socioeconomic

attainment, family formation and stability, psychological well-being, and

physical health. Agentic strategies may be more effective among those in

more favorable structural locations, but individual differences in these ca-

pacities are expected to influence life outcomes, net of the advantages or

disadvantages associated with structural position. According to this point of

view, agentic decision-making and behavior are especially important in late

adolescence and early adulthood because the points of entry to socio-

economic trajectories are established at this time.

These two fundamental paradigms, emphasized by macrosociologists and

social psychologists, are surely not mutually exclusive, since structural and

agentic processes operate in tandem. In fact, their coexistence and their

codetermination of social processes make interdisciplinary collaboration

essential. Greater mutual attention is especially called for between the ‘‘so-

cial structure and personality’’ branch of social psychology, concerned with

the manifestations of social location in enduring individual psychological

orientations and behavior; and the subareas of sociology concerned with the

‘‘micro–macro link.’’ The strong reliance on the rational choice paradigm

among investigators interested in micro–macro analysis has them closer to

economics and political science than to social psychology (House &

Mortimer, 1990). Integration of social psychological and sociological frame-

works is especially well suited to the increasingly prominent life course

perspective (Mortimer & Shanahan, 2003). To fully comprehend the pro-

gression of individual lives, we must not only study the changing historical

context and the variable and shifting structures of institutional

trajectories and the ways these impinge on individual actors (the family

life cycle, occupational careers, organizational career lines, etc.). We must

also consider the ways persons subjectively orient themselves to the oppor-

tunities and constraints that they encounter in particular times and places,

and the strategic actions they devise to reach their goals in particular struc-

tural contexts.

However, in the face of persistent evidence that socioeconomic origin

constrains life chances intra- and inter-generationally, it could be argued

that the expression of agency makes little difference. Do adolescents’ as-

pirations in fact exert a meaningful influence upon their attainments? Or,

alternatively, do their aspirations merely signify recognition, some-

times resignation, with respect to probable future destinations, given well-

understood consequences of placement in class structures and school tracks?

This question, in general, has perplexed scholars of stratification and the life

Agency and Structure in Educational Attainment 133

course for several decades (see, for example, Roberts, 1968, for an early

formulation). Whereas associations between psychological dimensions (val-

ues, efficacy, and identity) at one point in time and social locations at a later

date may be interpreted as providing support for the social psychological

paradigm, the alternative structural interpretation of such relations is like-

wise tenable.

It must be acknowledged at the outset that answers to these questions

must be conditional, and especially, historically specific. That is, the relative

importance of structural and social psychological determination of the life

course is likely to vary across time and place. Shanahan, Elder, and Miech’s

(1997) study illustrates this point very well. The Terman gifted men who

came of age during the Great Depression, facing a quite limited employment

market, showed strong proclivity toward postsecondary enrollment irre-

spective of their prior aspirations. But for a subsequent cohort, entering the

labor force in circumstances of expanding economic opportunity, early ed-

ucational values and aspirations were quite consequential for decisions to

pursue postsecondary schooling. Educational aspirations were thus more

predictive of higher educational attainment for the later cohort who en-

countered the postwar economic boom as they entered adulthood. Thus, the

capacity of personal dispositions and goals to influence subsequent attain-

ment depended on historically variable opportunities for choice.

Our examination of the process of attainment is especially strategic in the

contemporary US context where institutional bridges between school and

work are notably undeveloped (Mortimer & Kruger, 2000). High school

education is focused on college preparation, with little emphasis on voca-

tional certification, apprenticeship programs, or other structural connec-

tions between schools and employers. Young people develop their own

strategies in positioning themselves for the labor market. When the time

comes to find a full-time job, they must rely largely on their own resources,

friends, and relatives. Of course, those who come from more advantaged

families have greater resources at their disposal, which help them to com-

plete college and compete successfully for highly coveted professional and

managerial jobs. But even for these youths, pathways to college completion

and high levels of occupational attainment are by no means transparent or

certain. Youths’ efforts in school and their work behavior reveal two major

strategies of human capital acquisition, the first, more successful one,

through education; the second, constituting the more rapid route to full-

time employment, through early part-time work (Mortimer, 2003).

Investigations of the process of attainment have generally taken the

strategy of linking personal dispositions to outcomes measured at a much


later date (see Mortimer, 1996, for a review). For example, researchers have

predicted the features of workers’ jobs by their occupational reward values,

articulated as long as a decade prior (Mortimer & Lorence, 1979; Lindsay &

Knox, 1984). In the classic status attainment model, educational and oc-

cupational aspirations measured during the senior year of high school pre-

dict first jobs and subsequent jobs, attainments that often occur many years

later. Clausen (1993) reports that adolescents who were more ‘‘planfully

competent’’ had more satisfying occupational careers, more stable marriag-

es, and generally more gratifying experiences throughout their lives. Given

this general research strategy, the processes linking the personal orientat-

ions, measured at Point A, and the outcomes, measured at Point B, are

largely unknown.

In this paper, we argue that attention could fruitfully be directed to what

is inside this ‘‘black box,’’ to the processes, mechanisms, and behaviors

between Points A and B. We contend that researchers should examine the

actions that may express agency, actions that connect earlier psychological

orientations, such as goals and values, and subsequent attainments in the life

course. What are the immediate, day-to-day behaviors, provisional goals,

and shorter-term outcomes that link the widely spaced orientations and

outcomes that are the subject of most prior investigations? Moreover, we

argue that psychological orientations provide clearer links to agentic proc-

esses, and are more likely to be predictive of the attainments and conditions

of life that are of interest to social scientists, if they are domain specific. In

support of this claim, we have presented evidence elsewhere that economic

efficacy has stronger predictive power with respect to educational plans and

postsecondary school attendance than global efficacy (Grabowski, Call, &

Mortimer, 2001).

We draw on findings from the Youth Development Study (YDS), a

15-year prospective study of work experience and the transition to adult-

hood, to illustrate our approach. These highlight the interrelations of ed-

ucational orientations, patterns of employment during high school, and

educational attainment; as well as the ways adolescent future orientations

influence youth’s multifaceted pathways of transition to adulthood.

THE YOUTH DEVELOPMENT STUDY

The Youth Development Study (YDS) is the only long-term longitudinal

study designed to address the processes of structure, agency, development,

and attainment that surround early work experiences in adolescence.


The study began with a randomly chosen community sample of ninth grad-

ers enrolled in the St. Paul Public School District in Minnesota;1 1,010

students agreed to participate, and 1,000 filled out surveys in the first data

collection in 1988. United States Census data indicate that this site is quite

comparable to the nation as a whole with respect to economic and social

indicators (Mortimer, 2003).

Most of the analyses reported here use data collected over the years from

1988, when the respondents were in the first year of high school (ninth

grade), mostly at the age of 14 and 15; through 2000, when they were 26–27

years old. This panel study is still in process; the most recently collected data

were obtained in 2005. Extensive tracking and follow-up procedures during

each wave of data collection ensured that students who dropped out of

school after ninth grade, or moved to another school district, continued to

be followed. Data for the post-high school period, from 1992 to 2002, were

collected via mailed surveys. The surveys contained large batteries of ques-

tions focused on work experiences in several domains – in the family, school,

and neighborhood, as well as in formal work establishments. Extensive se-

ries of questions addressed the respondents’ psychological engagement, val-

ues, goals, and future orientations with respect to education, work, family,

and community domains, as well as key activities in each of these spheres.

As such, this study is well suited to examine the interrelations of structure

and agency as youths make their transition to adulthood.

Seventy-six percent of the initial panel remained through the 2000 data

collection. The retained sample was somewhat more advantaged than the

initial sample in terms of socioeconomic background and family compo-

sition (favoring the two-parent family), and retention was more likely for

females than males. The distributions of demographic characteristics across

the initial 1988 and 2000 samples, however, were substantially the same.

EDUCATIONAL ORIENTATIONS AS PREDICTORS

OF EARLY WORK INVESTMENT

The YDS enables assessment of lengthy pathways of attainment from

orientations toward schooling at the time of entry to high school, to patterns

of work investment during high school, to young adult educational and

occupational trajectories, and to 4-year college degree receipt.

Let us first consider adolescent educational achievement and engagement

at the age of 14 and 15, as predictive of very early labor force involvement


during the succeeding 3 years of high school. Most prior studies of ado-

lescent employment rely on the number of hours worked at a given time of

observation, or over a period of months or years. Our more nuanced meas-

ure attempts to capture more long-term strategic involvement in the labor

force, as it includes employment duration, measured in months, as well as

intensity, or the average hours of work during the full period of employ-

ment. Each was dichotomized: months, so as to distinguish near-continuous

employment from much shorter investment in work; and hours, so as to

separate those who on average work more than 20 hours per week from

those who work more moderately, limiting their hours to 20 or fewer. The

20-hour mark is widely considered the point at which employment becomes

‘‘excessive’’ for in-school American youth, interfering with other activities

and increasing the likelihood of problem behaviors (Committee on the

Health and Safety Implications of Child Labor, 1998).

Cross-classifying the two dimensions yields five work patterns during the

10–12th grades of high school: (1) the most invested, high duration/high

intensity workers; (2) sporadic, low duration/high intensity workers; (3)

occasional, low duration/low intensity workers; (4) steady, high duration/

low intensity workers; and (5) non-workers. Table 1 shows the distribution

Table 1. Patterns of Labor Force Participation During High School

by Gender.

Percentage Distribution Mean Months of

Work

Mean Hours of

Work

Total Boys Girls Boys Girls Boys Girls

Not working 7.0 9.9 4.6 0.0 0.0 0 0

Occasional

Low duration-low

intensity

23.7 23.2 24.1 9.8 11.7 578 650

Sporadic

Low duration-high

intensity

18.4 23.2 14.3 10.4 11.8 1,216 1,376

Steady

High duration-low

intensity

24.9 18.2 30.6 22.0 22.0 1,263 1,328

Most Invested

High duration-high

intensity

26.0 25.6 26.4 21.9 22.2 2,678 2,587

Total 100.0 100.0 100.0

N 887 406 481


of these patterns of work investment. Occasional, steady, and most invested

workers are about one quarter each of the total; sporadic workers constitute

18%. The fact that only 7% of the panel reported no work experience at all

demonstrates the high prevalence of part-time work experience, while school

is in session, in the American context. Combining school and work is a near-

universal experience in adolescent life. There are some notable gender dif-

ferences; boys are more likely than girls to be sporadic workers and to be

non-workers; girls are more likely than boys to be steady workers. For boys

and girls in the same investment category, however, mean months and hours

of work are quite similar. That is, high duration workers are employed

approximately 22 of the 24 months of observation and low duration workers

accrue only about half that number.

From an agentic perspective, adolescent work may be conceived as in-

strumental action directed to enhancing future prospects for socioeconomic

attainment (and other goals). Those who expect that they will accrue human

capital mainly through their efforts in the educational system would likely

downplay employment during high school. But those who think that they

have limited chances for academic success, especially with respect to higher

education, would likely seek alternative means of positioning themselves for

adult full-time work. Disinterest and poor performance in school, as well as

structural disadvantage (i.e., low socioeconomic status of origin) would

likely foster attempts to acquire human capital through early intensive em-

ployment. If educational achievement has rather low priority in the ado-

lescent peer culture, as Coleman (1961) suggested, one might also expect

those who are more highly oriented to their peers to be less engaged in

school and more attracted to work. School misconduct constitutes another

indication of disengagement and difficulty in the educational realm.

In prior investigations, we found that adolescents are not randomly se-

lected (or allocated) to the work investment patterns (Mortimer, 2003;

Mortimer, Oesterle, & Staff, 2003). For example, high levels of ‘‘educational

promise’’ in the ninth grade increased the likelihood that youth would pur-

sue steady or occasional patterns of employment, rather than more intensive

work investment, during the following 24 months of high school. Educa-

tional promise referred to the ninth graders’ level of engagement and success

in school: it included indicators of grade point average, academic self-esteem

(the perception of self as intelligent, a good reader, and high in school

ability), educational plans for the future, and intrinsic motivation toward

schoolwork. High-promise youths scored above the median on at least three

of these measures. Youths who pursued the ‘‘steady’’ work pattern during

high school were particularly advantaged vis-a-vis the educational realm.


They had high educational promise in the ninth grade, were unlikely to get

in trouble at school, and had relatively low levels of engagement with their

peers. Occasional workers were similarly characterized by high educational

promise and low school misconduct.

What is it about steady and occasional work that appears to attract young

people who are more strongly engaged in the educational enterprise, who

consider their classes more meaningful and interesting to them, and whose

early efforts in the educational realm (as indicated by grade point average)

are more successful? Surely, the ‘‘steady’’ pattern of working is the one that

is the most ‘‘balanced,’’ as it involves high continuity of employment while

at the same time, because hours of work are limited, permits simultaneous

investment in homework and other school activities. Interestingly, youth

who pursue this pattern are especially likely to say that they had sought

employment ‘‘to save for my future education.’’ Our further analyses show

that such ‘‘balanced’’ workers pursue multifaceted time use strategies

throughout high school, indicating substantial investment in paid work,

schoolwork, extracurricular activities, the peer group, and the family

(Mortimer, 2003; Shanahan & Flaherty, 2001).

Further indicative of agentic action, prior educational engagement not

only influences the pattern of investment in work; it also is linked to the

quality of work that is subsequently obtained. The features of their jobs

suggest that teenagers with limited proclivity toward school seek human

capital acquisition through employment. For example, low grade point av-

erage in the ninth grade is associated with greater advancement opportunity,

higher earnings, more learning opportunities, and a perception that work

confers greater status amongst one’s peers. Those youths who had lower

academic self-esteem early on had higher earnings in their high school jobs

and perceived greater work-derived status amongst their peers (Mortimer,

2003, p. 132).

Clearly, a set of trade-offs between school and work is revealed. Young

people who are more successful in school invest in work in a manner that

yields less in the way of extrinsic and intrinsic occupational rewards, but

enables greater investment in school and the capacity to pursue a well-

rounded, multifaceted, and achievement-oriented adolescent life style. In

contrast, youths who perform more poorly in this domain use their time so

as to obtain more learning opportunities in the workplace, greater imme-

diate economic reward, and higher status in the eyes of their peers (likely

comprised of others who have similar orientations to school and work as

they do themselves). Assessment of work investment and work quality after

the ninth grade tells much about how the expression of academic interests,


achievements, and goals upon entry to high school affect immediately en-

suing work-related behaviors that have quite divergent consequences for

socioeconomic attainments during the post-high school years.

WORK INVESTMENT AND TRAJECTORIES OF

SOCIOECONOMIC ACHIEVEMENT

A growth curve analysis applied to annual survey data collected in the years

following high school (1991–2000) has shown that youths who pursued less

intensive, steady and occasional work patterns during high school make

greater investments in higher education in subsequent years. That is, they

accrue more months of higher education during the first year following high

school, with background and socioeconomic origin controlled, and they

manifest fewer months of full-time work (Mortimer et al., 2003, p. 451).

High intensity work patterns were associated with lower initial levels of

educational investment, and also with greater investment in full-time work.

Furthermore, patterns of growth in months of education and months of

full-time work for the work investment groups confirm the merit of thinking

of these behavioral patterns in terms of strategic action. Low-intensity high

school workers, starting from a higher initial educational investment level,

have steeper losses in months of education during the 9 years following high

school. They exhibit more pronounced increases in full-time work. In con-

trast, high-intensity workers during high school are more attached to the

labor force during the first year after high school, and manifest a less steep

increase in months of full-time work thereafter. Lower intensity (steady and

occasional) workers start off with fewer mean months of full-time work. By

1998, however, they have caught up to the more intensively employed stu-

dents during high school. Their gains in full-time work are steeper than

those of their more intensively employed high school peers.

To what extent do these distinct patterns of time use following high

school-investment in higher education vs. full-time work – exhibited by the

teenage work investment groups-yield differential achievement? We can now

link early educational orientations, high school work patterns, and educa-

tional attainment. We have examined the youth’s capacity to achieve a

prominent educational goal, the BA (bachelor of arts or sciences) degree

(Mortimer, 2003; Mortimer et al., 2003). Consistent with classical models of

status attainment, we found that early school performance and aspirations

(indicated by the educational promise variable in the ninth grade), as well as


parents’ education, were linked with the receipt of a BA degree or higher in

young adulthood.

However, employment in a ‘‘steady’’ pattern of work during high school,

in comparison to more intensive involvement, also conferred a net advan-

tage in educational attainment in young adulthood, independent of socio-

economic background, educational promise, school misconduct, and other

predictors. Based on the most recent wave of data collection (2002), Fig. 1

displays the percentage of respondents that currently have obtained at least

a BA degree based on their prior engagement in work during the high school

period. Forty-four percent of young people in the steady work pattern

during high school had earned a BA degree by 2002, which was much higher

than the 17% of the ‘‘most invested’’ and 12% of the ‘‘sporadic’’ workers,

but also higher than that of the ‘‘occasional’’ and non-workers (33%).

Although the high school patterns of work are influenced by prior socio-

economic background and school performance, defined by gender, race,

parental education, and early educational promise, even when these and

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

No Work Steady Occasional Most Invested Sporadic Total

Fig. 1. Receipt of 4-Year BA/BS degree (2002) by High School Work

Investment Categories.


other relevant factors are controlled, teenage work patterns are found to

have independent influence on postsecondary educational achievement. The

‘‘steady’’ work pattern is found to be particularly salutary. Why do steady

workers during high school have the advantage in postsecondary educa-

tional attainment? We find evidence that subsequent work patterns are

critical. More recent investigation by YDS researchers (Mortimer & Staff,

2004) demonstrates that the work patterns the young people pursue in the

years immediately following the high school period appear to mediate the

relationship between the ‘‘steady’’ work pattern in high school and early

adult educational attainment. In Fig. 2(a) and (b), we show the average

months of higher education, full- and part-time work in the years right after

high school for the ‘‘steady’’ and ‘‘most invested’’ workers.

As shown in Fig. 2(a), the ‘‘steady’’ high school workers averaged nearly

identical months of postsecondary school attendance and part-time work

(between 7–8 months) in the 4 years immediately following the high school

period. Approximately 4 years after the end of high school, months of

school attendance and part-time work began to decline, while months of

full-time work increased rapidly. Fig. 2(b) highlights a very different pattern

of work and school involvement during the transition to young adulthood

for the ‘‘most invested’’ high school workers. For these youths, investment

in part-time work and school is rapidly replaced with increasing involve-

ment in full-time work. Despite the predictive power of work involvement

during high school with respect to educational attainment in young adult-

hood, we find that the inclusion of subsequent work patterns in this key

period of postsecondary education investment renders prior associations

insignificant (Mortimer & Staff, 2004). Especially for the ‘‘steady’’ workers,

the effective combination of working and studying is a familiar pattern by

the time they enter college. Since most college students have to maintain

employment to at least partially assume their educational (and/or living)

expenses, the time-use strategies these students learned earlier – of balancing

school and work – may enable them to continue combining school and work

roles without compromising their educational goals.

We thus find evidence of agency in the selection to work patterns, in

patterns of educational exploration and investment during high school, and

in the determination of future educational and work trajectories. Our strat-

egy of assessing domain-specific achievement-relevant indicators (e.g., eco-

nomic self-efficacy, educational promise) and achievement-relevant

behaviors closely following (i.e., patterns of labor force investment during

high school, steps taken to go to college) have yielded high returns with

respect to the understanding of trajectories of school enrollment and


0

(a)

(b)

1

2

3

4

5

6

7

8

9

10

19 20 21 22 23 24 25 26

Age

School

Pt-work

Ft-work

0

1

2

3

4

5

6

7

8

9

10

19 20 21 22 23 24 25 26

Age

School

Pt-work

Ft-work

Fig. 2. Average Months of Higher Education, Ft-Work, and Pt-Work in the Years

Following the High School Period for the (a)’’Steady’’ High School Workers, and

(b) ‘‘Most Invested’’ High School Workers.


full-time employment (Grabowski et al., 2001), and eventual BA receipt.

While we cannot fully rule out structural explanations, our analyses con-

sistently show that agency-relevant psychological precursors have signifi-

cant effects on immediate achievement-relevant behaviors, and that these

behavioral patterns are associated with educational attainment, net of

several indicators of socioeconomic origin.

AGENCY AND PATHWAYS OF TRANSITION

TO ADULTHOOD

The collaborative work of the YDS team is now moving to a more general,

and also more complex objective – to assess how diverse family and

achievement-related attitudes and plans, measured in high school, influence

successive configurations of roles in the succeeding years that constitute

latent pathways of transition to adulthood (see Macmillan & Eliason, 2003).

These pathways encompass not only education and work roles, but also

markers of adult family formation (leaving the parental home, marriage,

and parenthood). Thus, our earlier treatment of attitudes toward school as

precursors of work investment is expanding to consider further agentic

motivations and expectations related to the family and intimate relations.

Our earlier focus on trajectories of educational attainment and employment

is now also supplemented by consideration of multifaceted, interrelated

trajectories of educational, work, and family behavior, constituting diver-

gent pathways of transition to adulthood.

We use a two-staged latent class analysis set forth by Macmillan and

Eliason (2003; see also Clogg, 1995) to empirically investigate the simulta-

neous interplay among these social roles in the transition to young adult-

hood. This method is especially germane to a life course perspective, in that

it enables the identification of role configurations (and how they change or

remain stable over time) and the life paths that link these role configurations

through stages of the life course.2

Fig. 3(a)–(c) depicts the role trajectories within three general life paths.

Life path I (probability in sampled population ¼ 0.165), shown in Fig. 3(a),

characterizes a precocious or hastened transition to adulthood. During

adolescence, individuals in life path I have a relatively high probability of

parenthood (approaching 20%), as well as relatively low probabilities

of residing with parents (80%) and attending school (80%) in comparison

to those individuals in life paths II and III. An accelerated transition to


0.00

(a) (b)

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Ages 14-18 Ages 18-19 Ages 20-21 Ages 23-24 Ages 25-26

Age

Ex

pec

ted

Ro

le P

rob

ab

ilit

y

In school MarriedChildren Lives with parents

Steady, full-

time work


Steady, full-

time work

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00


Age

Ex

pec

ted

Ro

le P

rob

ab

ilit

y


Steady, full-

time work

0.00

(c)

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00


Age

Ex

pec

ted

Ro

le P

rob

ab

ilit

y

Fig. 3. Latent Life-Path Probabilities for (a) Precocious Transition to Adulthood, (b) Delayed Transition to Adulthood and

(c) Multifaceted Transition to Adulthood.

Agency

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145

adulthood continues in the years immediately following high school, as the

probability of school attendance declines sharply, while the probability of

steady, full-time work grows rapidly.3

Life path II (probability in sampled population ¼ 0.576), depicted in

Fig. 3(b), indicates a delayed transition to adulthood. During adolescence,

individuals in this pathway have a very low probability of parenthood.

Residence in the parental home (for at least part of the year) and school

attendance are prolonged in the years immediately following high school.

Family formation is also delayed (the probability of parenthood and mar-

riage are both under 30% at ages 25–26). Interestingly, sporadic, full-time

work is favored between the ages of 18 and 21 (not shown), perhaps in-

dicating summer work that is compatible with higher education, followed by

a rapid increase in the probability of steady, full-time work.

Finally, life path III (probability in sampled population ¼ 0.259), shown

in Fig. 3(c), suggests a ‘‘multifaceted’’ transition to young adulthood that is

neither rushed nor delayed. Similar to life path II, respondents have a high

probability of residing with parents and attending school. The work patterns

of individuals in life path III are also similar to those in life path II, as there

is a rapid increase in steady, full-time work in the years immediately fol-

lowing high school. However, the probability of having a child rises much

faster at ages 23–24 than in life path II (50% at ages 23–24, compared to just

9% for life path II); marriage exhibits a similar pattern. In addition, the

probability of residing with parents also declines more rapidly in early

young adulthood for this group.

To assess the impact of structural placement and agency on the prob-

abilities of following these three life paths, we regress individuals’ life path

probabilities on family background factors – race, gender, parents’ highest

educational level, family income, and family composition – and future plans

and goals for education, work, and family. As shown in Table 2, measures

of family background and socioeconomic status have a significant impact on

one’s life-path probabilities. In model 1, females and those whose parents

have lower levels of education and family income have higher propensities

for following a precocious life path. Males and adolescents whose parents

have higher educations and income have higher propensities for the slower,

delayed entrance into family and work roles, manifested in life path II, than

those with lower socioeconomic origins. Parental education is the only

significant socioeconomic predictor of the probability of following life path

III. Adolescents whose parents have higher levels of education have lower

propensities toward this multifaceted path. These findings highlight the sig-

nificance of structural advantage in the transition to adulthood.


Table 2. OLS Regression Coefficients of Latent Life Path Probabilities (n ¼ 498).

Precocious (Life Path I) Delayed (Life Path II) Multi-faceted (Life Path III)

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

B (s.e.) B (s.e.) B (s.e.) B (s.e.) B (s.e.) B (s.e.)

White (vs. non-white) �0.634 (0.34) �0.752 (0.33)� 0.474 (0.40) 0.531 (0.40) �0.086 (0.21) �0.047 (0.21)

Male (vs. female) �0.538 (0.23)� �0.552 (0.23)� 0.946 (0.28)�� 0.966 (0.28)�� 0.007 (0.14) 0.011 (0.15)

Parental education �0.418 (0.08)�� 0.285 (0.08)�� 0.318 (0.10)�� 0.207 (0.10)� �0.132 (0.05)�� 0.135 (0.05)�

Family income �0.287 (0.06)�� 0.241 (0.06)�� 0.351 (0.07)�� 0.320 (0.07)�� 0.045 (0.04) �0.050 (0.04)

Intact family (vs. non-intact) �0.211 (0.30) �0.042 (0.29) �0.009 (0.35) �0.134 (0.35) �0.019 (0.18) �0.028 (0.18)

Marriage importance – – 0.166 (0.20) – – �0.166 (0.25) – – 0.030 (0.13)

Parenthood importance – – �0.247 (0.19) – – 0.144 (0.23) – – 0.045 (0.12)

Family self-efficacy – – �0.090 (0.16) – – 0.158 (0.19) – – �0.087 (0.10)

Career importance – – 0.113 (0.20) – – �0.098 (0.25) – – �0.057 (0.13)

Occupational aspiration SEI – – �0.020 (0.01)�� – – 0.022 (0.01)� – – �0.001 (0.00)

Occupational designation – – 1.468 (0.49)�� – – �1.731 (0.60)�� – – 0.086 (0.31)

Economic self-efficacy – – �0.131 (0.06)� – – 0.102 (0.07) – – �0.010 (0.04)

High educational promise (vs. low) – – �1.100 (0.25)�� – – 0.848 (0.30)�� – – 0.023 (0.16)

Intercept 0.752 (0.46) �0.513 1.14 �3.814 (0.55)�� 2.463 (1.38) �0.940 (0.28)� �0.792 (0.73)

R2 0.19 0.26 0.15 0.19 0.03 0.04

�po0.05.��po0.01.��po0.001.

Agency

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147

Of particular relevance given our present concerns, the results show that

agency and planfulness are also important in predicting subsequent life

trajectories. According to our analyses, agentic orientations toward work

and education is what matters, not expectations and values with respect to

family life. Model 2 shows that the ninth graders with higher educational

promise and those with higher occupational aspirations have lower

propensities for the precocious path. Economic efficacy also reduces the

likelihood of precocious adulthood. Adolescents who specify what their

career might be in the future, indicating greater vocational crystallization,

have significantly higher probabilities of following this precocious life path.

High educational promise and high occupational aspirations upon entry

to high school predict higher probabilities of being on a path characterized

by ‘‘delayed’’ transitions into adulthood, which is not surprising given the

great educational investment and late exit from school that characterize this

path. It is interesting to note that adolescents who had greater occupational

uncertainty (indicated by failure to answer the question about their future

work plans) also have a greater propensity for path II. For those who do

know what occupation they hope to have in the future, however, the higher

their aspirations, the more likely they will follow this pathway. This pattern

of findings suggests that delayed entrance into adulthood can signify vo-

cational indecision, in which youth uncertainty leads to longer time spent in

school and later family formation. Alternatively, for those who have for-

mulated high aspirations at an early stage (age 14–15), delayed markers of

adulthood may result from a more focused postponement of full-time work

and family roles in order to attain the amount of education necessary for

high career aspirations. The longer incumbency of preadult roles and un-

certainty about future goals are often discussed in tandem when addressing

the contemporary transition to adulthood, as both foster extended explo-

ration and enable the postponement of adult role commitments. They do

not, however, always coincide. Moreover, delayed timing, when combined

with uncertainty, is likely to have a very different socioeconomic outcome

than delayed timing without uncertainty. With clear goals, young people can

maximally utilize the elongated transition to adulthood to develop their

human capital and otherwise groom themselves for future attainments.

Without clear goals, the lengthy transition to adulthood can be one of

‘‘floundering,’’ insecurity, and reduced achievement.

Attesting to the significance of adolescent agency, the addition of an

individual’s plans and expectations for family, career, and school in model

2 explains 7% and 4% more of the variance in the first two life path

propensities, respectively, than the measures of family background alone.


In addition, we find evidence that while the other socioeconomic predictors

remain just as strong, the impact of parents’ education on these two life-path

propensities is partially mediated by individual agency. These results suggest

that while social position does in fact play a part in ‘‘routing’’ individuals

into certain life trajectories and impacts the timing of onset of adult roles,

one’s goals and expectations for these roles also have independent effects on

his or her early life course trajectories.4 But unlike the other two pathways,

Table 2 also suggests that an individual’s propensity for a multifaceted life

path is not affected by the importance he or she places on family, education,

or career.

DISCUSSION

Our assessment of domain-specific indicators of agency and immediate ac-

tions taken in the pursuit of goals is shown to have payoff with respect to

understanding the process of socioeconomic attainment during adolescence

and the transition to adulthood in contemporary urban America. We there-

by obtain a glimpse of what may be inside the ‘‘black box’’ intervening

between the expression of goals and values in middle adolescence and life

course outcomes in early adulthood. Our study is, however, not without

limitations. We have confined ourselves to the study of a single panel, lo-

cated in a particular national setting, in one brief period of historical time.

Given its origin in the upper Midwest region of the US, this panel is not well

suited to the assessment of racial and ethnic differences. Moreover, we have

little information about structural location other than socioeconomic origin.

The St. Paul schools, from which the students were selected, do not have

visible ‘‘tracks’’ that may constitute institutionally located ‘‘ladders’’ of

achievement. In general, the study does not speak to structural placement

within the high school that could address the kinds of macrostructural ar-

guments we alluded to at the beginning of this paper.

Notwithstanding these limitations, our work does suggest distinct path-

ways through the early life course, implicating agency, achievement-relevant

educational and work behaviors, and early adult attainments. Moreover,

our analyses suggest that agency is intricately connected to adolescent em-

ployment patterns. A central contribution of our work is the finding that

adolescent employment is neither uniform nor randomly distributed during

high school. In fact, there are five distinct patterns of work investment that

are linked to social background, early educational promise, the quality of

work experiences, and early adult socioeconomic attainments.


Might the work investment patterns – steady, occasional, sporadic, most

invested, and none – be considered a kind of ‘‘emergent’’ structure? They

would surely be recognized as structural dimensions or patterns, as ‘‘insti-

tutional career lines,’’ if they were assigned by educational or other au-

thorities, or if they were clearly recognized and validated by their

connections to the school. However, unlike the German system of appren-

ticeship, no central authority decides how much students should work while

they are attending school. Instead, these work-investment patterns emerge

from the youth’s actions in the context of the naturally occurring, ‘‘free’’

labor market (Mortimer & Kruger, 2000). They arise from the multiple

decisions and actions of employers, parents, counselors, students them-

selves, and other ‘‘significant others’’ who persuade, advise or influence

them.

To the extent that social structures that regulate and channel behavior are

emergent from the very agentic behaviors of actors themselves, our attempts

to discover distinct influences of agency and structure become increasingly

complicated, perhaps futile, as all are part of an interrelated and finely

articulated process of life course emergence. However, our analyses show

how early goals and values are linked to work behaviors during high school,

which in turn have predictive power with respect to subsequent trajectories

of work, schooling, and educational attainment. Our most recent, and at

this time least fully developed analyses, take this strategy one step further –

to consider how a variety of agentic orientations affect life pathways

characterized by configurations of school, work, and family roles. We thus

explore how early family, school, and work orientations and goals affect life

paths of transition to adulthood. The preliminary analyses presented here

indicate that expectancies and values surrounding future educational and

occupational role enactment must be taken into account to fully understand

propensities for earlier or delayed timing of key markers of adulthood.

We have come a long way from early models of attainment that simply

linked adolescent educational aspirations with adult educational and occu-

pational attainments. In expanding the range of agentic orientations under

consideration, and addressing the intervening behaviors that enable goals

and values to be actualized, our understanding of agency and the transition

to adulthood is enhanced. While structure – here considered by way of social

background location – is certainly also important, we find considerable

evidence that the psychological constructs exert independent effects.

These investigations could fruitfully be extended to other realms – for

example, to trajectories of family stability, civic involvement, mental health,

and physical well-being. We trust that these reflections and analyses will


prod others to explore how structural location, psychological indicators of

agency, and agentic behaviors act in concert to produce contemporary life

course pathways.

NOTES

1. To assess whether participants (64% of those invited) differed from non-participants along common demographic characteristics, a probit analysis of thedecision to participate was conducted, including information from the 1980 Censusreported at the tract level to characterize the neighborhoods of all eligible families.Boys and older students (some of whom were retained in grade) were less likely toparticipate than girls and those who were the same age as most of their classmates.(Information about the gender and age of invitees was obtained from schoolrecords.) The racial, family structure, and socioeconomic composition of students’neighborhoods (indicated by median household income, receipt of public assistance,and educational and occupational status) were not related to participation (Finch,Shanahan, Mortimer, & Ryu, 1991).2. In the first stage, latent class modeling techniques are used to estimate the

unobserved latent role configurations of the markers at each age range (14–18,18–19, 20–21, 23–24, and 25–26), based on the cross-classifications of observed rolesat each. The second stage of the analysis generates a latent class model of the age-specific latent class configurations developed in the first stage. Latent life pathmodels are estimated using cross-classifications of unobserved, or latent, role con-figurations. These cross-classifications are derived from a ‘‘realization’’ of the latentrole configuration transition table (Macmillan & Eliason, 2003).3. The steady ‘‘full-time’’ workers averaged near continuous employment during

the sophomore, junior, and senior high school year at more than 20 h per week. Inthe years immediately following the high school period, steady full-time work in-dicates those respondents averaging 35 or more hours of paid work per week during11 or more months of the year.4. To see if these patterns differ by gender, we included interaction terms between

gender and each psychological measure in the model (not shown). These interactioneffects were not significant.

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154

NON-NORMATIVE LIFE COURSE

TRANSITIONS: REFLECTIONS ON

THE SIGNIFICANCE OF

DEMOGRAPHIC EVENTS ON LIVES

Frank F. Furstenberg

INTRODUCTION

A prominent tenet of life course research holds that ill-timed and unex-

pected events, non-normative transitions, and disorderly status sequences

have profound and lasting consequences for an individual’s success in later

life. Simultaneously rooted both in early demographic and sociological

studies undertaken in the middle part of the last century, life course theorists

have long argued that social timetables regulate status passages, creating

social support and expectations, and generating resources and rewards to

those who observe culturally mandated schedules (see, for example, Elder,

1998; Neugarten, Moore, & Lowe, 1965; Rindfuss, Swicegood, & Rosenfeld,

1987).

In their classic treatise on aging and social stratification, Riley, Johnson,

and Foner (1972) described how role transitions were socially orchestrated

through age norms that govern movement of populations into and through

social roles. In kinship-regulated societies, these rules determine the struc-

ture of generations and social reproduction. In modern societies, theorists





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155

such as Parsons (1954), Merton (1968), and Ryder (1965) explained that

social timetables are critical for determining and coordinating the basic in-

stitutions of society: the family, educational systems, and the labor market.

It is easy to understand how the assumption that unscheduled life events

have a singular power to cast a permanent shadow over the life course

became pervasive in the sociological and social demographic literature

grounded in a life course perspective. Many theoretical arguments appear to

support the notion that planning, timing, and orderly sequencing of role

transitions increase the probability of successful attainment.

First, in the middle of the past century, sociologists who worked in the

tradition of symbolic interaction emphasized that preparation for making a

status or changes was crucial to an actor’s ability to negotiate transitions.

The ideas of role preparation and anticipatory socialization, popular in the

sociological literature of the 1950s and 1960s, advanced the proposition that

successful acquisition of social roles required training, practice, and self-

definition (Becker & Strauss, 1956). It logically followed that correct timing

was an important feature of successful passage through a series of inter-

related status transitions. Social demographers, interested in the transition

to adulthood, began to examine the impact of timing of events on long-term

success in status attainment and being off-time conferred social risk

(Clausen, 1972; Marini, 1984; Winsborough, 1979).

With the work of Neugarten et al. (1965), social psychologists and soc-

iologists also began to explore how norms about timing operated to regulate

when and under what circumstances individuals would make significant

status transitions (Hagestad, 1990; Settersten, 1998). A line of research be-

gan to look into how institutional gatekeepers – family, schools, employers,

and legal authorities – helped to set and reenforce the social timetable by

allocating resources and rewards for staying on schedule and penalties for

violating age norms. The costs of either being early or late have been as-

sessed in a host of journal articles. As Elder (1984) ingenuously argued in

Children of the Great Depression, historical events such as wars or economic

downturns, outside the control of individuals, can drastically revise the op-

portunities for role acquisition and hence shape the course of lives. It is

evident in Elder’s work that he embraced the general assumption that timing

– fortuitous or ill – affects the structure of opportunities at both an indi-

vidual and social level, influencing the life chances of actors and cohorts in a

given society.

Another related line of theory about the life course argued that the se-

quencing of life events similarly had important consequences for success in

both the labor market and the family. Again, this proposition rests on the

FRANK F. FURSTENBERG156

assumption that when individuals follow predictable and socially organized

pathways, they are more likely to encounter success. Educational completion

prior to employment or marriage prior to first birth compared to the opposite

order are demographic examples that have received widespread attention by

demographers, economists, and sociologists. In a well-known article pub-

lished in 1961, Harold Wilensky (1961) showed how orderly as opposed to

disorderly careers were associated with positive consequences for a range of

economic outcomes as well as for psychological adjustment in later life.

Thus, there is a long history in the social sciences of believing that non-

normative or unscheduled transitions confer social disadvantage. My own

work was deeply grounded in the life course perspective, and on how the

timing and sequencing of transitions altered prospects for successful incor-

poration into adult roles (Furstenberg, 1976; Modell, Furstenberg, &

Hershberg, 1976). Of course this set of ideas, as Matilda Riley and her

coworkers observed several decades ago, were equally applicable to the

passage from adulthood to retirement.

TEENAGE CHILDBEARING AS

A NON-NORMATIVE EVENT

At the same time that Riley, Elder, Wilensky, and other social scientists were

setting out a life course perspective, I was beginning what has turned out to

be a life-long study of the social consequences of teenage childbearing. My

project in Baltimore, initiated in the mid-1960s, began more by happen-

stance than a deliberate intention to test a basic proposition of life course

theorists. However, I soon came to see the applicability of this theoretical

framework for examining the question of how and why premature parent-

hood occurs and its lasting implications for success in later life for young

parents and their children.

In my first book on the topic, I grounded my research in the idea that

early childbearing was a prime example of a violation of the normative

schedule for family formation. My point of departure was the oft-quoted

observation by the social demographer, Arthur Campbell (1968), who spec-

ulated that:

The girl who has an illegitimate child at the age of 16 suddenly has 90 percent of her life’s

script written for her.

In the decades that followed a huge literature has accumulated in the U.S.,

especially, but in Western Europe more generally, on the impact of early

Non-Normative Life Course Transitions 157

childbearing on the later life success of teenage parents and their children

(see, for example, Hayes, 1987; Brown & Eisenberg, 1995; Maynard, 1997).

It is beyond the scope of this essay to review this literature in any detail or

even recount the findings of my own 30-year longitudinal study of a pop-

ulation of teenage mothers who gave birth to a child while in their mid-

teens. Initially, my findings, like the work of others, appear to supply strong

support for the conclusion advanced by Campbell without the benefit of

much empirical evidence. But over time, with more sophisticated techniques

of data analysis, there is growing consensus among researchers in the field

that early childbearing did not produce the kinds of effects that life course

theorists would have predicted. While policy makers, the public at large, and

even teenage mothers themselves, still tend to believe that early childbearing

has deleterious, long-term effects on educational and occupational success

as well as mental and social health – both for the mother and the offspring –

the findings in the literature show that support for this conclusion is, at best,

ambiguous (Furstenberg, 2003).

Long-term, longitudinal studies reveal, contrary to expectations, that, at

most, parenthood during adolescence has only modest long-term impacts on

teenage mothers and their children when compared to mothers who delay

their first births and their children when the two populations are matched in

other respects. Thus, rather than affirming that non-normative demographic

transitions like teenage childbearing profoundly shape the course of later life

– that demography is destiny – the evidence seems to point in quite the

opposite direction. Non-normative events produce diverse consequences,

conferring disadvantage on some but seeming to perturb little or not at all

the pathways of others. I will return to the reasons why this is so shortly

after discussing the other example from another line of research that I have

engaged in, the impact of marital dissolution and remarriage for children’s

development.

MARITAL DISRUPTION AS

A NON-NORMATIVE EVENT

Long before the formulation of the life course perspective, social critics and

cultural commentators in the early part of the 20th century had reached the

conclusion that marital disruption has devastating effects on children’s life

chances. Their literature argued that divorce, as a life event or transition,

when viewed from the perspective of children, has many of the same prop-

erties as early childbearing. It thrusts children into a family situation which


is usually unplanned and for which they are typically unprepared. It de-

prives them of resources in the form of parental time and money, and it

restricts their ability to acquire social skills from the absent parent. More-

over, since divorce is frequently followed by remarriage or cohabitation

(and sometimes further disruption), it exposes children to a succession of

transitions that are often accompanied by geographic moves, changes in

household routines, and shifting rules and regulations (Furstenberg &

Cherlin, 1991; McLanahan & Sandefur, 1996). In short, there is abundant

reason to believe that divorce as a life event fundamentally compromises

children’s immediate and long-range welfare.

In recent years, the social and personal costs of divorce for children have

been the topic of a considerable body of research. As in the case of early

childbearing, the popular impression of what this research has shown di-

verges from what many professional researchers have concluded, though it

can hardly be said that consensus exists. A few researchers, most notably in

the U.S., Wallerstein and Blakeslee (1989) and Waite and Gallagher (2000),

have concluded that divorce is every bit as detrimental to children’s life

chances as has been long believed. However, a growing number of re-

searchers in the field have reached the conclusion that, surprisingly, marital

disruption is relatively weakly linked to later life outcomes (Moynihan,

Smeeding, & Rainwater, 2004). Like many of my peers, I would be inclined

to say that the long-term impacts of divorce are modest to moderate in the

aggregate, which is to say that divorce confers risk for some but certainly

not for most children whose parents’ marriages break up during their

childhood years (Furstenberg & Cherlin, 1991; see also Cherlin, 1999).

Now, if I am correct in my characterization of the evidence from studies

carried out over the past several decades, we might reach the conclusion, at

least based on these two prime examples, that the consequences of non-

normative events outcomes in later life are surprisingly minor in light of life

course theory. In the remainder of this paper, I will first try to explain why

theorists overstated the adverse impact of ill-timed and out-of-sequence

events. Then I will try to revise the theory in ways that are more consistent

with the evidence. Doing so suggests an agenda for future research, a topic

that I develop in the final part of the paper.

WHEN THEORY AND EVIDENCE COLLIDE

Looking back at the two case examples that I have mentioned, they share

some prominent features. Both could be characterized as social problems


where researchers have been working against a backdrop of heated public

discourse and have joined the public in treating evidence in a cavalier fash-

ion: the theory seemed so compelling that the evidence collected was un-

critically evaluated. Even if Arthur Campbell, a respected social

demographer, had engaged in hyperbole in assessing the impact of early

childbearing on the subsequent life course of young mothers, surely could

any reasonable person doubt that teenage parenthood does not irreparably

compromise the life chances of young mothers and their offspring? And,

even if Judith Wallerstein was a self-declared advocate for reducing the toll

that divorce takes on children, is it reasonable to question her conclusion

that divorce produces lasting damage for those who experience the loss of a

parent and grow up in a single-parent household or, worse yet, experience a

cascade of family change (Ahrons, 2004)?

And, indeed the findings in each of these separate lines of research initially

seemed to conform to conventional wisdom. In the 1970s and early 1980s,

study after study showed that teenage mothers had extraordinarily high

rates of school dropout, reliance on welfare, subsequent fertility, unstable

marriages, and so on. Similarly, Wallerstein’s conclusions, based on a small

clinical study in the 1970s, found widespread support from other research

showing a powerful association between divorce and a range of adverse

outcomes for children. However, both areas of research – as has been true in

many studies of unscheduled and ill-timed events in the life course – have

suffered from potentially fatal methodological problems.

Economists have long recognized the problem of unobserved differences

or endogamy, the inability to identify causality between an event and its

consequences. In the 1980s, some sociologists too, began to come to grips

with the issue (Lieberson, 1985). Simply put, teenage childbearing, divorce,

and other such events, do not occur randomly within a population. To the

contrary, these events occurred selectively to individuals or couples who

differ in a wide variety of ways that distinguish them from their counterparts

who are able to avoid the event. I need not dwell on this point because it has

become so widely understood by social scientists that few fail to appreciate

the burden it places on reaching causal conclusions about the links between

events and their consequences.

Over the past couple of decades, a wide variety of methodological strat-

egies have been employed to tease out causality in assessing the influence of

timing and sequencing in the life course. Controlling for prior group dif-

ferences has become standard, but it is widely known that such an approach

inevitably over-estimates casual effects by omitting unmeasured differences.

Fixed effects models in longitudinal research partially address this problem.


Similarly, researchers have used statistical methods for distinguishing

between exogenous and endogamous influences. Finally, researchers have

devised clever quasi-experimental approaches to identify true casual links.

None of these approaches, by itself, has completely solved the problem of

selection, but together they have demonstrated that a healthy share of causal

influence of ill-timed or out-of-sequence events is social selection rather than

true cause.

Specifically, much and probably most of the presumed effect of teenage

childbearing on later life outcomes appears to be spurious (Maynard, 1997).

That is, if we could postpone the births of teenagers by 5 years, it would

have little effect on their life chances because most would not end up going

to college merely because their first birth occurred later. Or, when they

eventually became pregnant, they would not be married or otherwise ca-

pable of supporting their child as a single parent. Paraphrasing Campbell’s

assertion, we might say that if a pregnant women in the US is poor, black,

and has been a low-achiever in school, 70 percent of her life script may be

written for her – whether or not she has a child as a teenager or in her early

20 s. Early childbearing usually does not help young women to succeed, but

neither does it cause them to fail, having the crushing impact on their lives

that Campbell, and many other social scientists, have hypothesized.

The story about the consequences of divorce for children appears to be

similar but not yet so clearly documented. In the first place, the evidence on

the detrimental effects of divorce on children cannot be so easily disregarded

in part because researchers have not yet found as many ways to rule out the

influence of unobserved differences. Divorce is the outcome of a process that

typically involves protracted parental conflict, economic uncertainty, and

unresolved sexual and interpersonal problems. Moreover, it frequently oc-

curs in unions formed by couples with prior emotional, social, and economic

problems. Sorting out how much the divorce, per se, adds to or complicates

the circumstances of children who are already vulnerable and in conflict-

ridden households, is a challenging problem. Still, there is little doubt that

many of the presumed effects of divorce are in fact results of differences that

occur prior to the event itself such as poor parenting, marital strife, eco-

nomic and social circumstances, and the like.

If my evaluation of the literature on these two examples is correct, it raises

a huge issue for life course researchers than has not been adequately ad-

dressed: why are the effects of non-normative events not as large or perhaps

as long lasting as life course theory has predicted? In the second part of this

paper, I will try to answer this question. In doing so, I will suggest mod-

ifying, if not abandoning, the overly simple model that assumed that the


timing, scheduling, and sequencing of life course transitions invariably

produce powerful and persistent consequences for the course of later life.

In its place, I argue for a more intricate theory of when and why non-

normative transitions have lasting effects or are of little consequence over

the long term.

COMPLICATING THE PICTURE: THE MODERATION

OF LIFE EVENTS OVER TIME

The problem posed requires an understanding of why non-normative events

do not produce the large and lasting impact that theory predicts? Answering

this question requires a close examination of how individuals, families, and

social systems react to the occurrence of ill-timed and poorly sequenced

transitions such as teenage childbearing or marital disruption. Accordingly,

I will draw examples from findings of my own research and the work of

others to illustrate why and how our theory needs to be revised to take

account of the findings of life course research.

Several different reasons might explain the seemingly anomalous findings

to which I have referred: First, it is possible that these events only affect a

small number of the highly vulnerable individuals and that most individuals

are relatively robust and impervious to non-normative transitions. This has

sometimes been referred to in the literature as sources of resiliency in de-

velopmental psychology.

A second possibility, which could be considered a variant of the first, is

that there is an inevitable distribution of responses to the occurrence of non-

normative events in any given population: some individuals react adversely

while others may actually benefit from the unexpected transition; still others

may have little or no response. In other words, a series of conditions and

circumstances at the individual and social level moderate the impact of non-

normative transitions.

Finally, we might imagine that individuals are widely affected by the event

but in time these responses fade much as ripples in a pond diminish with

distance from the initial splash. Over time, individuals can take actions to

repair or recover from the initial response to a non-normative event, off-

setting any potential damage done. In fact, these explanations are not en-

tirely mutually exclusive and may all have a part in explaining why the

impact of non-normative transitions is not as large as many believed. Let us

consider each in turn.


Back in the 1960s, when the first interviews were conducted in the

Baltimore study, the then pregnant teenagers and their mothers were asked

how they initially reacted when they learned that they were pregnant.

Recalling the event, the majority of parents replied that they were extremely

angry and upset and their daughters, similarly, reported that they were

frightened and distressed. A typical response by the pregnant teens went

something like this:

‘‘I was shocked and sad. I knew my mother would be furious. I was really

scared of what she would do when she found out.’’ However, when then

asked how they now felt (because the first interview occurred several months

after they learned that they were pregnant), most replied that they had

become reconciled to, if not excited by, the prospect of parenthood in the

interim. The fact is that many of the parents, who were angry at first,

softened their reactions and most of their daughters, fearful of their parents’

reactions, learned that they would be assisted and supported by their

families.

By this time, a number of parents summed up their change of heart by

saying of their daughters’ circumstances, ‘‘Everyone is entitled to make one

mistake.’’ The pregnant teens, in turn, had begun to feel as though they

could recover from their misstep by making a greater commitment to their

future and the future of their child. Young women, who had or were about

to enter marriage, were especially positive by the time of the interview be-

cause they believed, as many pregnant teens did at the time, that marriage

would put them back on course. However, even the teens that did not

contemplate matrimony became more sanguine about their prospects of

rectifying their situation. Many learned that they could stay in school, re-

ceive special services, or would receive help from the baby’s father. On both

psychological and sociological levels, most families mobilized their resources

to respond to the impending challenge by a redefinition of the situation.

I would not want to claim that this redefinition neutralized the potentially

adverse effects of early childbearing, but it does suggest that actors reacted

neither passively nor uniformly to early childbearing. Many were able to

mobilize in response to becoming pregnant in ways that might mitigate some

of the impact. And, apart from their ability to redefine the situation, the

young mothers and their parents began to explore a range of different pos-

sible adaptations to becoming pregnant, including marriage, returning to

school, and the realignment of family roles, to mention but a few.

True, the unplanned pregnancy forced a series of unanticipated changes in

the life course of the teenagers, but the changes were not always negative.

Some adolescents claimed at the time as well as in subsequent interviews


that the pregnancy was a turning point in their lives. It made them grow up

faster, take themselves more seriously, and realize that they had to take

seriously their new responsibilities. In many cases, they reported improved

relations with their parents, greater engagement in school, and an enhanced

sense of self (see Furstenberg, 1976; Furstenberg, Brook-Gunn, & Morgan,

1987). As an indication of these changes, the interviewer who watched the

young mothers from one wave to the next, often commented how much and

how quickly they had grown up after they became parents.

Of course, considerable variation occurred in the years following the first

birth of the teen mothers in both their reactions to parenthood and the level

of support provided by families and the surrounding community. Among

those who hastily married, some entered seemingly viable unions while

others found less suitable or compatible partners. Similarly, for those who

remained single, some were able to find their way back to school and even-

tually into the labor force while others were not. Young mothers who found

their way to a special school for pregnant teenagers were especially likely to

complete high school in the five years following the birth of the first child.

The young mothers used public and private support differently. Some, who

received public assistance, made good use of it by returning to school; others

devoted themselves to caring for their children until they were of school-age

at which point they either went back to school or found employment; still

others were unable to mobilize to gain economic independence in the years

that followed the birth of their first child.

I, along with many other researchers, have devoted considerable attention

to explaining the sources of these varied life trajectories of teen mothers

after the birth of their first children. Clearly, part of the source of variation

lies in individual attributes of the young mothers themselves. Physical and

mental health, cognitive skills, attractiveness, motivation, flexibility, and a

host of other attributes, no doubt, figured into their ability to respond to

their changed circumstances. In the language of developmental psycho-

logists, some mothers were more vulnerable and others more resilient in the

face of a new challenge.

Families too, possess different levels of resources and support to offer to

the young mothers in the way of time, money, guidance, social contacts, and

the like. They differed in family-based social capital, the ability to mobilize

support for their children and cultural capital, and knowledge about the

social world. The families, and the young mothers, were also differently

embedded in neighborhoods and communities, and thus had differing access

to resources to cope with premature parenthood. My collaborators and I

have shown what an important part access to services, programs, mentoring,


and the like played in determining successful adjustment in the immediate

years following the birth of a first child.

These conditions all affected the likelihood of other related transitions

such as marriage, return to school and graduation, and most notably, the

birth of a second child. The probability of a second birth was much greater

for those who married than those who remained single and differed among

the single women depending on whether they returned to school or not.

While information on individual level psychological resources was limited,

clearly, some women were more prone to having subsequent unplanned

births than were others, a potent predictor of long-term success. In time,

some women curtailed their fertility by sterilization and others became in-

fertile, but higher order births proved to delay or deter the ability to gain

economic independence and invest in children already born. In some cases,

they led to ruptures with families, who were initially willing to offer assist-

ance but whose patience or resources depleted when demands persisted or,

worse yet, increased.

The longitudinal evidence suggests that there is no single path to recovery

nor is there any point from which recovery becomes irretrievable. To make

matters even more complicated, new events such as the formation of a new

partnership, the death of a parent, or a changing employment situation

continually perturb trajectories for better and for worse. At each interview –

and there have been seven in all – we discovered that the circumstances of

some mothers would change direction. Moreover, sometimes, gains or losses

might occur in economic fortunes without affecting other domains of suc-

cess such as interpersonal relationships, mental and physical health, or self-

definitions. The correlation of different indicators of success was only

modest, complicating our ability to tell a single story about successful and

unsuccessful pathways. In short, it is not easy to point to a single set of

strategies that invariably paid off in later life success. Women who married,

for example, did not necessarily fare better than those who remained single

in part because the marriages were so brittle and were often accompanied by

higher rates of fertility. Conversely, seeking public assistance did not prove

to be a poor strategy if women made use of welfare to get more schooling.

To discriminate successful from unsuccessful trajectories, we have to under-

stand how the strands of the life course are interwoven through a series or

chain of small decisions, looking at the intentions of the young mothers as

they faced certain choices or encountered different possible pathways. An

ongoing and continuous feedback took place between life decisions, how

they worked out in reality, and how situations were subsequently defined

and interpreted by the young mothers, their parents, their partners, and


their children. Without seeing the interplay between the intentions, deci-

sions, and immediate consequences, it is difficult to make sense of the chain

of strategies that evolved over time to understand how they worked to

produce particular outcomes. Many of our analytic techniques are insen-

sitive to this complex process and most of the data that we collect are not

sufficient to carry out an analysis that splices together qualitative accounts

(motives and interpretations) and quantitative evidence about decisions and

their consequences. Doing so properly requires new ways of examining

longitudinal information and new techniques for blending qualitative and

quantitative information.

Even with the crude analytical techniques that I have employed in the

Baltimore Study, it is possible to see the emergence of different patterns of

success at mid-life. First, women who married early and were able to forge

viable and rewarding unions clearly did better than anyone else in the study,

but they were only a tiny minority (under 10 percent of the sample) of the

young mothers in the study. Case studies of these women showed that they

held different attitudes about marriage, came from families with more stable

unions, and possessed a greater array of interpersonal skills. They also

married men, often the fathers of their first child, who possessed similar

attributes. Thus, marriage confers few benefits unless those who marry

possess the resources, attitudes, and skills to make it work out. Lacking

some or all of these qualities, most entered a succession of unions that did

not work out well and indeed left the young mothers and their offspring

worse off than many of their never-married counterparts. Some women

rapidly took themselves out of the mating game, choosing to pair with men

in short-term sexual relationships or they eschewed men entirely. When

these women did so to invest in their children, their offspring frequently did

well. Alternatively, when they withdrew from men without economic or

family support, the children often fared poorly.

Second, women who were able to manage to control their fertility did

much better in later life, as did their children. Fertility control, like mar-

riage, required motives, resources, and opportunities. For example, some

women curtailed their childbearing by staying out of unions or avoiding

relationships with men. Others sought sterilization after they had a first or

second child. Only a small number were able to use contraception success-

fully. To understand the implementation of fertility control, we must look at

several different possible routes that the young mothers took as well as

considering the fact that some were simply more fecund than were others. In

sum, women achieve low fertility in many different ways or fail to do so for

several different reasons, such as their inability to manage relationships with


men, their poor skills at using contraception, or their religion beliefs that

prevented them from seeking abortion.

Third, work success, not surprisingly, came to those who returned to

school and gained credentials, but other women, who were not terribly

successful in gaining schooling, managed to find particular economic niches.

Again, we concluded that there was a strong link between educational suc-

cess and economic independence, but the pathways to educational success

and economic independence varied quite a bit among the women in the

study. Some returned to school immediately, others went to work and then

back to school, and still others only went back to school later in life.

Although we do not have enough cases to test the efficacy of these different

strategies, it appears that they all seemed to produce positive effects.

Unquestionably, the women who had the cognitive, psychological, and so-

cial support to gain more schooling and develop labor market skills fared

reasonably well compared to those who failed to build human capital.

Complications only grow when we try to link the success of the mothers

to their offspring. Without a doubt, links exist but they are rarely straight-

forward. Success in employment, for example, was associated with better

outcomes for children, but the correlation was relatively weak. Mothers’

actions on their own behalf sometimes led to improvement in their children’s

life chances, but very often they did not because the mothers’ investments in

themselves came at the cost of time and involvement in their children. Some

of the less successful mothers devoted themselves to their children, putting

their hopes in their children’s, rather than their own, careers. This strategy

occasionally paid off, at least if we believe some of the accounts of children

who succeeded despite growing up while on public assistance. Many of the

children when they reached early adulthood reported that their mothers had

held up their own experiences as teen parents as a negative example to their

children. Their mantra became: ‘‘Don’t follow in my footsteps.’’

Working with data from the Baltimore Study, as I have for almost four

decades, has taught me to appreciate how much actors construct, interpret,

and make meaning of their actions in ways that have powerful consequences

for both themselves and other immediate members of their families. Most of

the teen mothers that I followed over time felt that they had become preg-

nant too early and entered parenthood too soon. Almost all wanted their

children to take a different pathway. It is no easy matter, however, to

explain why some were able to follow their mothers’ wishes while others

became second- (or third-) generation teen mothers. The ability of parents to

translate their desires to their children differs greatly depending on their

parenting skills, their resources to keep their children on track, the support


that they receive by others in their household and community, and of

course, the children’s individual and social circumstances. It is one thing to

say that lives are inextricably linked across the generations, it is quite an-

other to show exactly how such linkages play out within families.

One might conjecture that variability in outcome is greater in the pop-

ulations most susceptible to non-normative events. No doubt, low-income

and marginalized populations are more vulnerable to these events for both

structural and perhaps psychological reasons. However, I would argue that

the broad contours of what I have reported about the difficulty of predicting

trajectories of teen mothers and their offspring apply more broadly to less

vulnerable populations. John Clausen (1972) used the term loose coupling to

refer to the varied pathways that can be taken when events occur in the life

course. Not all researchers who have studied divorce have appreciated

Clausen’s observation that casual links between powerful events and their

consequences can sometimes be quite faint. Many of the same reasons why

teen parenthood does not inevitably have adverse consequences for parents

and children apply as well to divorce.

Divorce, like teenage childbearing, may have positive as well as negative

effects. The most obvious of these is that it often curtails chronic conflict

between parents or leads to the removal of a dysfunctional parent from the

household. Children may bond more strongly with the residential parent (or

even the non-residential parent) when the couple is no longer living together.

They may have opportunities to acquire new competencies by assuming

greater responsibilities in the household. Of course, none of these possibil-

ities is without potential costs, but researchers often ignore them in the

literature on divorce, which assumes that the consequences for children are

invariably bad (for an exception, see Barber & Eccles, 1992).

Quite apart from the potential benefits that divorce might confer to chil-

dren, researchers have shown that children in the same family react quite

different to marital disruption (Hetherington & Kelly, 2002). Depending on

their personal interpretations of the reasons for the divorce, their relation-

ships to the residential and non-residential parents, the reactions of siblings,

and a host of other conditions that moderate the impact of marital disso-

lution, children’s reactions to the event vary enormously. Moreover, parents

differ in how they manage the divorce, their capabilities for handing lone

parenthood, their economic circumstances, and the amount of support that

they can garner from others. All of these conditions affect the way that

divorce impinges on a child’s life both immediately and in the long term.

The immediate reaction of children to the divorce of their parents does

not necessarily signal how they may adapt to the divorce over time, as events


that follow the divorce in both the parents’ and children’s lives can over-

shadow the significance of the marital breakup and change its meaning for

children.

Suppose, for example, that upon the breakup of parental marriage, chil-

dren encounter a series of new and unstable family circumstances. Compare

their circumstances to the opposite situation where the parents are able to

work out a collaborative pattern of parenting and are committed to re-

maining single. Alternatively, we can imagine that one or another remarries

and the stepparent proves to be a capable partner and parent for the child.

These different scenarios can lead to very different outcomes and, in turn,

influence the child’s definition of the impact of the divorce on their lives.

Just as in the case of teenage childbearing, a number of different con-

ditions can moderate the impact of divorce by either heightening or atten-

uating its long-term consequences for parents and children. The initial

‘‘definition of the situation’’ (that is, the way that it is first experienced) sets

in place a response trajectory, but evidence seems to suggest that the initial

response is not a reliable gauge of the long-term consequences. And, in any

event, more than we frequently recognize, early responses are the product of

pre-existing individual differences and social conditions often linked to the

nature of the transition; these pre-existing differences are partially or largely

responsible for the slope of the response trajectory. Thus, we must take great

care to take into account the fact that non-normative events often occur

selectively to individuals and families that are already vulnerable. Assessing

even their initial impact has proved to be a challenge precisely because it

requires sorting out endogenous and exogenous casual factors.

Subsequent, events may often reinforce the impact of non-normative

transitions, but they often have just the opposite impact: they may offset the

initial adverse reaction because individuals continue to respond, react, and

adjust to their life situations. In this paper, I have attempted to explain why

and how this can happen. Let me conclude by summarizing some of the

most important conditions that can redirect response trajectories over time.

1. Individuals increase their motivation and commitment in response to a

non-normative event. In other words, they mobilize to counteract the

predictable negative impact of a non-normative transition.

2. Others in their social networks, especially family members and friends

may increase support and resources to help them recover from the event.

3. Social agents intervene providing services and access to opportunities

that might not otherwise have occurred. Policies can be designed to re-

duce the potentially adverse effect of a non-normative transition.


4. The experience of the event increases previously undeveloped personal

and social capacities. Non-normative transitions can, and often do, pro-

mote development.

5. Subsequent life events offset and compensate for initial disadvantages.

Over time, other events and transitions may reverse the fortunes of those

who encounter non-normative transitions. This can happen merely from

good fortune or because of one or more of the other reasons listed above.

CONCLUSION

I have suggested that theorists describing the impact of non-normative

events – ill-timed, unscheduled and out-of-sequence transitions – have sub-

scribed to a simplistic and overly deterministic model of how the life course

evolves over time. They have not given enough attention to the role of actors

in defining and moderating the impact of such transitions nor have they

taken adequate account of how a wide range of conditions moderate the

impact of non-normative events on subsequent life pathways. Drawing from

both my own research and the work of others, I have identified a series of

important mechanisms that often moderate the impact of non-normative

transitions. Consideration of these mechanisms may help to explain why

events such as teenage childbearing and divorce do not cast as long or as

dark a shadow in the subsequent life course as is often believed.

I have also contended that future research on the life course must take

fuller account of how circumstances are defined and responded to by actors

as well as examine objective markers of success and failure. Failing to do so

means that we ignore the process by which individuals experience the event

and hence the ways that they seek out and discover possible corrective

actions. The management of life course events is an under-investigated topic

that brings together personal agency, social support, and opportunity struc-

tures in a common theoretical framework that may improve our under-

standing of how life courses are constructed and how individual

development occurs in response to both normative and non-normative

transitions.

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socialization. American Journal of Sociology, 70, 710–717.

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from birth to death: interdisciplinary perspective (pp. 137–152). Boulder, CO: Westview

Press.


THE SECRET OF TRANSITIONS:

THE INTERPLAY OF COMPLEXITY

AND REDUCTION IN LIFE

COURSE ANALYSIS

Katherine Bird and Helga Kruger

1. DEFINING THE ISSUE

In a sociological perspective, transitions focus our attention on a segment of

the life course in which a biographically significant change of social posi-

tioning occurs. This apparently simple statement masks the complications

involved, which stem from framing the concept within and between disci-

plines, within and between methodologies, and within and between life

course assumptions.

Two recent articles (Elder, Johnson, & Crosnoe, 2003; Marshall &

Mueller, 2003) outline the development of the life course perspective from its

humble beginnings to an established discipline. Analogous to the continual

development of overall life course theories, the perception and conceptu-

alisation of transitions has also diversified. The endeavour has certainly not

been completed, since complexity arises on many fronts: on the macro-level,

status passages refer to institutional resources and guidelines for attaining a

new state (Heinz, 1996), while, on the micro-level, status passages are per-

sonally conceptualised and modified by biographical actors. Deciphering the





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10006-9

173

occurrence, timing and variability of transitions (cf. Hogan, 1981; Rindfuss,

Swicegood, & Rosenfeld, 1987) reveals nation-specific diversity, and so does

the explanation of how transitions link institutions and actors by defining

timetables, and entry or exit markers. And when Elder (1998, p. 958) es-

tablishes ‘‘transitions are always embedded in trajectories that give them

distinctive form and meaning’’, he draws attention to another level of com-

plexity: the arbitrariness of their interpretation in both the interdependen-

cies of status accumulation and the different perspectives related to internal

(biographical and personal) success stories and external evaluations.

Given the extent of these problems within an emerging discipline, it is no

wonder that more often than not in the literature on transitions we en-

counter pragmatic strategies associated with specific theoretical objectives.

Examples include:

1. To draw attention to the course and prolongation of transition processes

before the subsequent status is reached, which calls to mind the contro-

versies about individualisation, such as the loosening of institutional ef-

fects on biographies (Beck, Giddens, & Lash, 1996), or just the expansion

of periods of personal uncertainty about status passages from one insti-

tutional grip to the next (Heinz, 1999; Blossfeld, 1985).

2. To consider the biographical timing of events, an endeavour that has been

greatly enriched by approaches to the interlacing of biological and social

clocks as internalised external gatekeepers for windows of opportunity,

and calls for the investigation of multiple temporal dimensions as frames

of reference (Settersten, 2003; Settersten & Mayer, 1997; Mortimer,

Oesterle, & Kruger, 2004).

3. To focus on the often neglected socio-personal framing of transitions and

stages by the standardising forces of transitions in one’s partner’s life

which afflict your own life (Moen, 2003a; O’Rand & Farkas, 2002;

Sørensen, 2004). Hagestad’s conceptualisation of durations and transi-

tions (Hagestad, 1992, see also Hagestad & Neugarten, 1985) deciphers

even ‘countertransitions’ in order to underline the occurrence of status

changes arising from transitions on the part of socially relevant associates,

such as being transported into grandmotherhood by a daughter’s giving

birth, or being deprived of your marital status by your partner’s death.

Reduction of transitions occurs by means of theoretical assumptions and

the correspondent definitions of transition markers as well as by means of

the postulated empirical abundance of the segment under scrutiny. On the

basis of the theoretical approaches outlined above we can identify ‘inline

KATHERINE BIRD AND HELGA KRUGER174

transitions’; for example, the analysis of transitions with respect to one life

course domain (see example 1 above), in contrast to ‘competing transitions’;

for example, the analysis of context norms and biographical opportunities

(see example 2) – very often relevant in female decisions in favour of or

against motherhood – and ‘coupled transitions’; for example, transitions in

linked lives (see example 3).

Reduction occurs methodologically by means of the data collection and

analysis tools employed. In this context, it is important to point out that

from the event history analysis perspective – and this applies easily to de-

mographers as well – a transition is minimised by defining it as the occur-

rence of a precise event, such as leaving school. Thus the terms ‘transition’

and ‘event’ become synonyms. The other extreme is to maximise transitions

into changes over a specific duration in life, such as the transition from

childhood to adolescence, which opens the door for broader develop-

mental perspectives and associated scholars. Between the two extremes are

transitions of much shorter duration, such as from company manager to

being unemployed, or from secretary to housewife. And although Elder

points out that every transition can be broken down into ‘‘a succession of

mini-transitions or choice-points’’ (1998, p. 958), we suggest moving away

from analysing the ordering of transitions in a linear fashion (cf. Sackmann

& Wingens, 2001) and to reconsidering complexity instead.

This endeavour leads us to concentrate on transitions within and between

domains of social participation. Given that the individual’s simultaneous

participation in other social domains will both affect the transition under

study and be affected by it; we wish to advance Levy’s very early suggestion

(Levy, 1977, further developed in 1997) to conceive of the life course as a

movement through varying participation profiles. The fact that pathways

are layered within life domains introduces personal and structural contin-

gency into transitions, which can easily be overlooked. By seeking to reduce

complexity, we might accentuate the education, employment and retirement

history, and simultaneously neglect transitions within the dynamics of fam-

ily life, or within the health and illness history of a life. However, each of

these histories are subjected to normative and institutional transition pro-

grammes that themselves follow a logic specific to that domain of life, and

which ‘dissect’ the life course into relatively separate but still interwoven

patterns of behaviour. Even in the case of apparently discrete transitions,

the analysis might have to take into account the social interplay of change

and continuity with respect to other life domains, such as, working while

still at school (Mortimer, 2003), or, more formally, undergoing vocational

The Secret of Transitions 175

training as dual participation (Kruger, 1999), or being at work while retired

(Moen, 2003b).

Individuals experience transitions as governed by the different logics of

distinct life domains, and it is the interplay of life domains within transitions

that attracted our interest. Against this background, we propose that there

are ‘interlacing transitions’ that require attention. If we assume that a tran-

sition in one life domain is interlaced with simultaneous participation proc-

esses in other life domains, some of these abide by socially defined criteria of

mutual exclusion producing contradictory forces that push or pull in dif-

ferent directions. We argue that we need to look deeper and further in order

to disentangle the various strands of structure and agency that underlie a

specific life course arrangement.

Introducing the category ‘gender’ facilitates the analysis of interlacing

transition processes. We have therefore decided to focus on women’s

lives. We will first outline the theoretical basis as their dual integration into

society via the labour market and the family to provide a starting point for

unravelling the dynamics of two strands of the life course, which, in coun-

tries with a strong life course regime like Germany, produce gender in-

equality (Kruger, 2003). The empirical data presented in the third section

offer a new look at the classical transition from employment to homemaking

on the birth of a child. The change of perspective reveals mainstream status

subsumption processes, which mask the different continuity demands of

distinct life domains. The second set of empirical findings focuses on the

example of official and actual marital status to examine the dissonance

between institutional status definitions and the reality of lone motherhood.

In the final section, we draw some conclusions that challenge sociologists to

reconsider the social structuration of life as well as the adequacy of our

analytical tools for capturing the hierarchical dependency of transitions

between different social domains.

2. LAYERED LIFE COURSE PATTERNS:

THEORETICAL ADVANCES ON THE FEMALE CASE

The simultaneous relevance of more than one life course domain in women’s

lives was highlighted 18 years ago by Regina Becker-Schmidt, who coined

the phrase ‘dual integration into society’ (doppelte Vergesellschaftung,

Becker-Schmidt, 1987). She was referring to the contradictions inherent in

women’s socialisation and participation in society as both a future worker


and as a caregiver. Much has been said about the objective problems of

seeking to combine these two tasks, especially in terms of timetables: for

kindergarten, school, paid work and so forth. The qualitative research con-

ducted by Becker-Schmidt and her colleagues also revealed the subjective

problems associated with attempting to solve this balancing act: the women

studied were ambivalent towards both types of social participation. They

could neither say that paid work was the most important thing in their lives

nor were their families. To quote the title of the book: ‘‘One is not enough –

both are too much’’ (translation of the original title: ‘‘Eines ist zu wenig –

beides ist zu viel’’, Becker-Schmidt, Knapp, & Schmidt, 1984).

The significance of these findings was to underline how separate fields of

social participation are simultaneously relevant in individual life courses,

but framed in a contradictory manner. Although Becker-Schmidt and others

have made an important contribution to understanding the complexity of

women’s lives, their ‘dual integration into society’ and the ensuing ambiv-

alence is, in most life course approaches, ignored in favour of conflict-

reducing simplicity.

Entanglements are eliminated by defining one possible life domain as

dominant for each sex and thus blending out all others. The ‘economic

theory of the family’ suggests that the primary function of the family is best

achieved by a gendered division of labour between a male breadwinner and

a female caregiver (for a concise summary of this approach see Blossfeld &

Drobnic, 2001).1 Catherine Hakim (2001) has developed the ‘preference

theory’, which turned the conflicting demands of paid and family work into

an orientational concept of a ‘‘personal work-lifestyle preference’’ (Hakim,

2001, p. 16).2 Following in the early life course tradition of comparing the

timing of events across birth cohorts, Karl Ulrich Mayer indirectly sup-

ported this view by stating in 1998 that on the basis of indicators such as age

at marriage or at first birth, or the family dependent phasing of employment,

female life courses were ‘‘highly standardised and relatively homogenous,

but – with the exception of marriage – not institutionalised and differen-

tiated’’ (Mayer, 1998, p. 447).

The reduction of structural ambivalence in women’s processes of societal

integration to ‘rational choice’, ‘preferences’, or the statement of a lack of

institutionalisation and differentiation in female life courses per se prompts

the question of whether these presumptions primarily result from a theo-

retical and methodological artefact relating to the underestimation of the

interlacing of transitional regulations in the two life domains of the labour

market and the family. To examine this question further we will explore

some empirical findings concerning two family related transitional segments


in female life courses: the transition to motherhood and the transition to

lone parenthood.

3. EMPIRICAL FINDINGS

3.1. Interlacing Transition I: A Baby Break

The event ‘giving birth’ undoubtedly marks a turning point in patterns of

female employment and family life, but is usually reduced to just the oc-

currence of a series of labour market exits and re-entries.3 If we move away

from a construction of the life course as a sequence of discrete transitions

towards a conceptualisation of interlacing transitions involving changing

participation profiles in the juxtaposed layers of life domains, we uncover

societal transition programmes that seek to subsume specific life course

domains under their own particular logic. In searching for the strands rel-

evant to transitions around motherhood, we therefore decided to integrate –

in addition to labour market exits and re-entries – the instruments developed

by the state for shaping labour market ‘timeouts’ on the occurrence of the

‘event’ of giving birth, and thereby incorporated the ‘negative space’ occu-

pied by leave-taking rules into the analysis.

This broader view allows us to consider three historical forces with op-

posing or contradictory objectives that may act out their effects on ‘pref-

erences’. These are (a) The wide-scale expansion of part-time jobs that was

intended to pull mothers back into paid employment, whereas (b) occupa-

tional career structures push them out. In addition, the introduction and

extension of extended periods of (c) maternity and parental leave will pull

and push them for sequenced time spaces into one domain or the other.

To investigate the effects of the three life course structuring principles we

collected life course data on 2,130 West German women from three ap-

prenticeship-completion cohorts: 1960, 1970 and 1980.4 At these dates the

women were aged between 18 and 20 years and had just completed training

in one of the top 10 occupations for women in Germany.5

During the window of observation for these three cohorts, three types of

maternity or parental leave were in force. Initially, new mothers were for-

bidden from working shortly before and after a birth under the motherhood

protection law (Mutterschutz of 8 weeks). In 1979, a motherhood leave

(Mutterschaftsurlaub, literally ‘motherhood vacation’) of 6 months was in-

troduced. The extension beyond 8 weeks was optional, as was the longer

‘child-rearing leave’ (Erziehungsurlaub, literally ‘child-rearing vacation’)


introduced in 1986. Initially lasting for 10 months, child-rearing leave was

extended in a stepwise fashion to 3 years in 1992 and could be taken by

mothers or fathers. With the introduction of this last measure, new oppor-

tunities for configuring the duration of leave and changing gender-specific

participation were established.

For the women studied, the median duration between completing training

and the birth of the first child varied between 5 and 8.5 years, depending on

occupation and cohort. To capture the effects of the different leave regu-

lations, the mothers were regrouped into ‘motherhood cohorts’ according to

which type of leave they could take when they gave birth: the motherhood

protection cohort, the motherhood leave cohort and the child-rearing leave

cohort.

During the period covered by the study, nearly no change was observed in

the integrational capacity of the labour market segments open to people

trained in the 10 occupations investigated. However, the expansion of part-

time working did cause considerable change, which was accompanied by

shifts in the cultural contexts of women’s lives. For the oldest women in the

sample, the dominant norm was that of the stay-at-home wife and mother

(as Hradil put it in 1992: The ‘Golden Times of Normality in Family Life’.

See also Born, Kruger, & Lorenz-Mayer, 1996). Gradually, this norm

transformed into acceptance of employment during motherhood, especially

part-time, accompanied by the official recognition in the state’s statistics

of the true extent of mothers’ paid work assisting in the family business

(Willms-Herget, 1985). The then empirically proven younger cohorts’ visibly

higher employment participation fitted well into individualisation theories

that highlighted how, as old ties that anchor females firmly into a specific

position in family life have dissolved, diversity and discontinuity both within

individual life courses and between gendered life courses in general have

increased (Beck & Beck-Gernsheim, 1994).

In spite of these well-known interpretations, by looking only at women’s

actual leave-taking behaviour,6 we found that with the new options for

lengthening and sharing parental leave, female life course patterns became

more, not less, standardised – and they followed occupational career pat-

terns, not part-time ones. The proportion of mothers who stopped working

(that is, took leave or quit their job) within 6 months of giving birth varied

according to both motherhood cohort and to occupational career patterns,

as shown in Fig. 1.

The ‘stopped working’ percentage shows that the motherhood protection

cohort (black bars) not only did not stop work as much as normatively

expected – normative frames would lead us to expect nearer to 80% at least


(see Wurzbacher, 1951; Myrdal & Klein, 1956/1968; Pross, 1976) – but in

addition, they did so in line with the specific opportunity structures asso-

ciated with the occupation trained for. The inclusion of the other two

motherhood cohorts highlights how – as the length of leave available was

extended – an increasing proportion of women stopped working when they

had a child, but again in the same occupationally specific manner.

It should be noted that in the younger cohorts, leave taking was optional

and not compulsory. But we can see that not just the birth itself but the

state’s framing of this event has encouraged women to realise this option at

this particular time. The frequency of the transition from paid employment

to family work within 6 months of giving birth has in fact increased – in

spite of the assumption of the younger cohorts’ higher commitment to paid

work.

Additional event history analyses of our data on the rate of transition

from employment to a family break on the birth of a child confirmed that

changing leave entitlements have significantly (at the 1% level) increased the

likelihood of a labour market exit for mothers in the younger cohort,

whereas those not entitled to extended leave were least likely to make this

transition (Bird, 2004). Other data also support this finding. Beckmann and

Kurtz (2001), for example, report that 84% of West German women took

advantage of longer leave regulations.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

Nurse Bank Office Ind. office Hairdresser Dr. asst Wholesale Spec. sales Retail Hotel

Occupation trained for

Pro

port

ion

of

moth

ers

stop

pin

g w

ork

(%

)

Mothers' protection Motherhood leave Child-rearing leave

Fig. 1. Percentage of Mothers Stopping Work within 6 Months of Giving Birth to

their First Child by First Occupation and Motherhood Cohort.


These findings contrast with Mayer’s (1998) assumptions about the lack

of institutional effects on transitions in women’s lives, as we clearly discern

two equally powerful institutional programmes at work – leave regulations

and occupational specifics – which contradict each other, but both also

disprove the effects of an assumed lower or higher commitment to paid

employment. How can we conceptualise these facts?

(a) From status definitions to ambiguity. An event within one strand of the

life course (such as giving birth) can be a catalyst for a labour market exit,

but is also interwoven with a second strand – the state’s regulation of

maternity leave. But the second strand (state regulations) used to remain

hidden or its effects were underestimated, since many of these exits are

masked if only the event of giving birth is related to the mother’s labour

market status. In Germany, the institutionalised participation in the labour

market does not formally change while on leave, since leave-takers still

enjoy the status of being employed.7 If the resulting status subsumption is

analysed with a method that permits only one activity at a time (such as

event history analysis), the conclusions drawn will be inaccurate. If, how-

ever, we consider the mothers’ factual participation profiles rather than their

formal employment status, in the younger cohorts we actually find an in-

creased proportion of stay-at-home mothers dedicated to family work and

not to paid employment.

(b) From events to echo effects. The state’s regulation of parental leave

produces a time frame for the transition back to employment, which impacts

on the layered female life trajectory. With the exception of the small per-

centage of women who did not take advantage of prolonged exit oppor-

tunities, our data revealed the high degree of conformity between length of

leave available and length of leave taken (Bird, 2004).8 These findings raise

questions for both psychologists and sociologists concerning the meaning of

a longer break for the early period of motherhood.

We can assume that an extended absence from the labour market affects

the mothers’ perception and memory of the early period of motherhood. It

may provide the feeling of having now shared enough time with the baby, so

that the mothers can return to paid work without a bad conscience, or such

an absence could lead to increased insecurity about a return to the public

sphere in general, or influence their confidence in their ability to perform well

at work. Research is also required into how mothers personally anticipate

reconciling work and the family as the child grows older (see the timing of

debates on rising ‘overprotection’ in mothering, such as Schutze, 1986).

Qualitative data collection could provide illuminating answers to these

questions if the women in the oldest cohort who took a 3-year break under


the motherhood protection law were compared with the youngest mothers

who took a 3-year child-rearing leave. More generally, the factual masking

of the interlacing transitions between paid and family work has meant that a

whole range of important research questions have been neglected.

(c) From labour market to occupational structure. In all three cohorts the

mothers’ behaviour follows an occupational pattern. Hence, a third strand in

this transition is, undoubtedly, the first occupation trained for.9 In the anal-

ysis illustrated here, the hairdressers stopped work far less often than, for

example, the bank or office employees. Another interesting finding on the life

course structuring effect of the first occupation was that the fertility rates also

varied between the women who trained for different occupations. The ex-

planation for these phenomena is to be found in the market rules (labour

supply) and normality assumptions (including age norms) for the labour

force in the German service sector (for more details see Born et al., 1996).10

Occupational specifics are reflected in whether or not women had children

and whether they stopped working when they did so. Their occupations

negate the rules of cultural change in the acceptance of mothers’ employ-

ment. Over the different cohorts, women’s behaviour is dominated by the

same occupational patterns, and so are the effects of the motherhood tran-

sitions on the long-term female employment outcomes (Bird & Gottschall,

2003). All these findings are undoubtedly evidence of the impact of insti-

tutionalisation programmes in female life courses and equally refute the

importance of preferences in Hakim’s sense.

In summary, our analysis highlights that it is important to expand the

depth of focus – in this case to discover the overlapping of the state’s

mediating regulations with the peculiarities of the occupation trained for.

This implies overcoming or questioning official employment status defini-

tions and identifying the interwoven strands of a ‘setting in change’, also

including psychological processes, and to investigate their influence on fur-

ther transitions around labour market exits and entries.

The remarks on the need to question official status definitions that reduce

complexity also holds for our second empirical example, although it refers

to another transition in a setting in change.

3.2. Interlacing Transition II: The overlapping realities of

marital status and lone motherhood

The following section focuses on motherhood and marital status, which are

the classical transitions for describing change in female life courses across


time (see, for examples, Huinink, 1991; Tolke, 1989; Mayer, 1998). In

Germany per official social welfare law (and transportation into everyday

life knowledge as well as into life course research), the legal status ‘married’

excludes the co-status of lone parenthood, which being single, divorced or

widowed would admit.

In order to investigate the category ‘marital status’ in its relevance for

exits or entries into lone motherhood, our research project took a two-fold

approach. In the standardised questionnaire, we first asked the respondents

for dates of marriage, divorce, widowhood, and several pages later we of-

fered a life-history calendar which opened up the chance to mark time slots

in simultaneously occupied fields in their lives (see Note 4). We compared

the durations of lone motherhood with the data on marital status

(Erzberger, 2001a). A total of 261 women provided apparently contradic-

tory information on their marriage and lone motherhood histories. An

analysis of these cases identified four combinations of both (illustrated in

Fig. 2), all consisting of somewhat confusing participational patterns be-

tween the beginning and end of periods of being married and being a lone

parent (i.e., solo).

The chart is a symbolic representation of the arrangements of layered

periods found in the data; the length of the blocks symbolises the overlap of

statuses in question rather than the actual durations in each. The vertical

lines represent the event of official change in marital status. The example

Type 1

Married

Solo

Type 2

Married

Solo

Type 3

Married

Solo

Type 4

Married

Solo

Fig. 2. Correspondence of Marital Status and Lone Motherhood.


illustrates vividly the lack of synchronicity in interwoven strands of the life

course and poses a challenge for the classification of these phenomena:

In Type 1, the phase as a lone mother starts before the marriage ended,

and ends before a new marriage is recorded. This pattern was found for 114

or 44% of the lone mothers. The average time spent as a lone mother while

still married and not married again was 2 years and 2 months. If we follow

the traditional logic of transitions, the question arises of which event ‘causes’

lone parenthood and which one ends it. The law would say: from the date of

divorce until the date of a new marriage. This clearly does not reflect the

empirical reality of women’s lives. If we consider lone parenthood to start

with the separation of the partners, then we are still confronted with the

question of when does separation start: When the partner moves out, when

the decision to move out is made, or when the first irreconcilable differences

emerge? And then, the same questions arise with respect to the event of

ending lone motherhood.

Type 2 is even more confusing. The phase of lone motherhood occurs

during the marriage. Such a pattern even occurs fairly often; it was observed

for 72 mothers (28% of the sample). How does this situation arise? One of

the project members suggested that the husband might be absent for a time

because of his work, perhaps as a captain on a ship. Another member

suggested that he could have been in prison. A third proposed that a mar-

ried woman separates from her husband and becomes a lone mother, then

later she moves in with a new partner and so ceases to be a lone parent, but

is still married to the first man.

The interpretation of the two remaining types (marriage as an unsynchro-

nised interlude in lone motherhood or continuing lone motherhood after

marriage) also results in unsatisfactory explanations that we do not need to

expand on here. We maintain that it is impossible to decide which of the many

plausible explanation is in fact the correct one, and whether only one expla-

nation would be valid for all the women in a group. The important lesson to

learn from this example is that if we use marital status as a proxy for lone

parenthood, then analyses based on this assumption will be inaccurate.

Similar to our empirical findings on the interlacing transition to moth-

erhood, other data from our previous qualitative studies within the Bremen

Life Course Centre revealed many more important transitions in women’s

lives, which again are swept over by the dominance of analytical conven-

tions. Examples include:

� Women who left paid employment in order to reduce stress factors and

so better their chances of becoming pregnant, but it did not happen. In


this case, a transition had been made (employment to homemaking) but

the anticipated event (birth) had not occurred.� Women who returned to employment to be financially independent in

anticipation of a divorce, but it did not happen.� Again others who left paid employment, because their child was doing

badly at school, but no visible event emerges.

Evidently, transitions occur in many cases, but they often cannot be asso-

ciated with a visibly corresponding explanatory event, which influences the

perception of transitions in sociological research. If the analysis of transitions is

minimised to the study of events, then we end up shooting wide of the mark.

The empirical examples presented here have sought to clarify what is

meant by the term ‘interlacing transition’. In the first example, status sub-

sumption masked empirical reality. In the second example, using an official

definition for an event masked its occurrence or non-occurrence. Both ex-

amples show that the reduction of complexity in female life courses can

easily seduce researchers into superficial analyses and to working with mis-

leading causal assumptions about the logic of transitions within institutional

constraints. Therefore, instead of seeking to reduce complexity we have

focused on areas where complexity has been reduced to such an extent that a

distorted perception of reality has emerged.

4. DISCUSSION: INTERLACING TRANSITIONS –

WEAVING THE STRANDS OF A LIFE COURSE

Transitions involve structural interventions in the shaping of the life course

and precipitate reorganisation processes affecting the self within layered

frames of social order. Many authors have made statements with respect to

the timing and sequencing of transitions, to their relation to trajectories, and

to gatekeeping practices of institutions that provide standardisations and

normality assumptions about which transition is expected, which regulations

are to be followed, and how goals are personally and socially combined (for

an overview see Mortimer & Shanahan, 2003; Heinz & Marshall, 2003).

Although it is an indispensable goal of sociological theory to reduce the

complexity of individual lives to a manageable explanation of human life

course patterns, simplification should not be taken so far that the distinguishing

features of lives lived simultaneously in several social fields are eclipsed.

To draw attention to the interweaving of different strands of social partici-

pation we introduced the term ‘interlacing transition’. The reintroduction


of the necessary intricacy has implications for a multi-dimensional study of

transitions. We have to take into account:

1. Theories shape awareness and define timeframes. Transitions are most

appropriate for probing the multi-layered facets of the life course. But it

depends on our basic assumptions about life course patterns whether we

consider it worthwhile to devote sufficient attention to the turning points

that we discover and permit a large enough window of observation for

sounding out their depths. As the empirical examples showed:

� Regarding the birth of a child as the core of an interlacing transition

reveals the regulations and expectations for behaviour to be found in the

working environment (occupational specifics), the legal regulation of paid

employment (leave regulations) and social norms (cultural role models for

motherhood). These deeper layers of the transition are often overlooked

because the theoretical treatment of simultaneously relevant participation

domains has been underdeveloped.� A foreshortened perception of the transition to lone motherhood that only

focuses on official status is not able to reveal the variation in real expe-

rience found if we consider this to be an interlacing transition. Enlarging

the window of observation beyond the moment of change in official status

aids the identification of the topology of the transition and may indicate

social change.

Other researchers, primarily in North America, are following a similar

strategy. Phyllis Moen (2003b) and her associates (Han & Moen, 2001) are

studying retirement as an interlacing transition. The strands that are rel-

evant to the timing and form of retirement include career pathways and the

opportunities and restrictions presented by living with a partner. As Moen

(2003b, p. 269) pointed out, ‘‘[the transition to retirement] is a process

embedded in a number of overlapping contexts’’.

2. Interlacing transitions can reframe contemporary discourse. Searching for

and following the strands of an interlacing transition is a good guide for

sociological life course research to expose the contradictory forces of the dif-

ferent societal programmes that can point the life course in different directions.

The ambiguity and contradictions inherent in the female life course offer

ample opportunities for identifying the interweaving of different social

domains. The lack of a theoretical framework for managing the contradic-

tory forces produced by interlacing transitions has meant that, at least in


Germany, the analysis of male life courses might have been oversimplified.

The increase in more flexible working practices and the emerging ideal of the

‘‘new father’’ indicate that the contradictory strands in women’s lives are

increasingly finding their counterpart in men’s lives. A new framework for

analysing the issue of reconciling work and the family, for example, may

facilitate its perception as a problem that involves mothers and fathers

equally, along with their employers and the legal opportunities and restric-

tions imposed by the state.

3. The role of the state. The perception of interlacing transitions underlines

Mayer and Muller’s (1986) early proposal that, if we are to fathom the

significance of transitions for the shape of the life course, then – at least in

countries with a high degree of life course standardisation – we need an

analysis that incorporates the role of the state. They elaborated the effects of

state-run social insurance introduced to protect the individual worker

against the risks of work-related accidents, illness, unemployment and old

age. Other researchers have focused on the state’s intervention in the life

course by structuring the education, employment and retirement trajectories

(Kohli, 1985; Blossfeld, 1985; Heinz, 1999). The state’s power to anchor

status definitions in legislation and in formal distinctions (in certificates and

documents) should not be underestimated. But this is not the end of the

story. We can expand on these previous analyses to conceptualise the state

as an important intervening power in shaping interlaced transitions in the

family and employment trajectories.

On the other hand, the second empirical example on lone motherhood

highlighted the inadequacy of relying solely on official status definitions

to determine when and whether co-transitions in family life occur.

Even though the state can frame a specific transition (both normatively

and administratively), we should not, without reason, assume conformity

of individual action. As Elder et al. (2003, p. 8) pointed out ‘‘Individuals

choose the paths they follow, yet choices are always constrained by the

opportunities structured by social institutions and culture’’. Too rigid a

focus on the dimensions of state action can mask the processual character of

transitions or blend out turning points that should be considered transitions,

but have not yet found their appropriate place in theories of the life course.

4. Re-framing the instruments of transition analysis. We can only capture the

contingent and multi-layered nature of interlacing transitions if we use em-

pirical tools that are more sensitive to complexity.


The collection of quantitative data needs to be reconsidered with respect

to the extent to which a questionnaire permits the recording of synchronicity

within different layers of the life course. Analogously, the analysis of these

data needs to include opportunities for reversing the practice of subsuming

simultaneous status positions under one dominant one.

In classic event history analysis, using large data sets such as the German

Socio-Economic Panel (SOEP), the state space is defined by the researcher

as consisting of mutually exclusive statuses. Discrete transitions from one

state to another can be analysed, but not the attainment of the target state

without leaving the origin state. Although such methods of data reduction

certainly facilitate its analysis, we would argue that this approach not only

masks multi-layered activities, but also defines partial transitions as of sec-

ondary importance for the timing and ordering of events.11 In order to

overcome the temptation to reductionism inherent in this method, addi-

tional methods of data analysis are necessary to assess the long-term dis-

tribution of non-discrete participation profiles. Explorative techniques such

as optimal matching of large-scale data sets (Abbott, 1995; Erzberger,

2001b; Aisenbery, 2000) are extremely useful in mapping out the actual

sequence of activities during a specific timeframe and thus also offer a

means to compare the layeredness of life sequences with simple event pat-

terns (Erzberger & Kluge, 2000; Erzberger, 2001b). Even within an event

history analysis, exploratory analyses can facilitate the development of a

parametric model (Wu, 2003).

In interpreting the results of either explorative analyses or parametric

modelling, however, the limitations of a quantitative approach become ap-

parent. The relationship between quantitative and qualitative research is

more than a simple either-or (cf. Kluge & Kelle, 2001). Without the con-

tribution of our previous qualitative research in uncovering the variety and

steering forces in female life courses we would have missed the importance

of interlacing transitions in women’s lives, and the discovery of new ques-

tions to be studied. In addition, it would have been much harder to con-

struct an instrument for quantitative research that did not force women to

subsume the layers of their lives into just one, but to expand on events and

durations in juxtaposed time spans.

5. Transitions: Definitions in progress. We believe it to be self-evident that

the conceptualisation of the term ‘transition’ should not lead to a search for

a formal definition, but is an ongoing process being pushed forward by the

interaction of theoretical and methodological advances. As we learn more


about how to study and understand lives, we automatically move away from

the simple guillotine-like perception of transitions.

Theories of the life course based on a reduction of complexity have cer-

tainly been useful in mapping out a rough plan. However, they can lead to

the ‘disappearance’ of specific transitions resulting in an oversimplification

that could explain the suggestion that the female life course is ‘‘not insti-

tutionalised and differentiated’’ (Mayer, 1998, p. 224), or Hakim’s attribu-

tion of women’s decision-making to wide-reaching preferences for either

employment, or family work (or both just part-time).

We can agree with Glen Elder (1985, p. 31) who prefers the term ‘‘tran-

sition’’ to ‘‘life event’’ because changes in the life course are not just sudden

events but are embedded in interceding, preceding and succeeding processes.

However, Elder’s reasoning stops just short of the mark because it does not

consider how an interlacing transition implies simultaneous changes of so-

cial positioning in other layers of the life course. An event in one strand may

have repercussions for the others, which themselves are continuously push-

ing or pulling the life course in specific directions.

The proposal to perceive transitions as the interlacing of transformation

processes in participation patterns and thinking in terms of status config-

urations rather than status changes will, hopefully, lead to more precise

empirical and theoretical approaches that will consign oversimplifications to

the past.

NOTES

1. In a recent edited volume comparing transitions between paid and family workin an international context, the authors referred to the economic theory of the familyas ‘‘the most highly developed and influential theory in this field so far available’’(Blossfeld & Drobnic, 2001, p. 16).2. For an extensive critique of this theory see, for example, Crompton and Harris

(1999).3. This can lead to neglecting the partner’s labour market opportunities, an over-

sight that is being corrected by, for example, studies on couples’ careers (Blossfeld &Drobnic, 2001; Elder, 1996; Marx-Ferree, 1997); on the pathways into retirement(Moen, 2003b; Han & Moen, 2001); or simply the husband’s income which, as ourstudy of older women revealed, provided the best argument in hindering women’sintentions to leave or return to paid employment (Born et al., 1996).4. The research was conducted within the Special Research Program ‘Status Pas-

sages and Risks in the Life Course’ at the Bremen Life Course Centre and wasfinanced by the Deutsche Forschungsgemeinschaft. The survey was restricted toWest Germany because the structure and normative context of the labour market for


women in East Germany was too different during the historical period investigatedto allow a meaningful cohort comparison.The data were gathered at the end of 1997 with a life-history calendar that was

designed to find out what the women actually did, rather than their official status atany particular point in time. For this reason we asked them to record all theiractivities at different times, rather than making them decide which was their principalactivity. By asking about different types and hours of employment, motherhoodleave and family breaks, and time spent supporting and caring for dependent familymembers, it was possible to gather information on parallel layers of activity overtime. Interestingly, there were many women who stated that their family activitycontinued long after they had returned to work, confirming the lasting importance ofdual social participation, while at the same time highlighting how fallacious it is toask women to only name their principal activity.5. The occupations were nurse, qualified bank employee, trained office employee,

industrial office employee, hairdresser, doctor’s clerical and medical assistant, officeemployee in a wholesale and/or export firm, specialist sales assistant (in a baker’s orbutcher’s), general retail sales assistant and hotel receptionist. In all three cohorts,between 65% and 76% of all female apprentices trained for one of these occupations.6. Men who have made real use of the new opportunity to take child-rearing leave

do not require further consideration here, because between 1986 and 2001 no morethan 2% of eligible fathers took leave (Schneider & Rost, 1998; BMFSFJ, 2002).7. Women (or men) who take child-rearing leave are now entitled to 3 years of

leave, or even longer if another child is born before leave ends. These women stillhave an employment contract and the guarantee of a job to return to. So, formally,these women are counted as employed in statistics used to calculate the employmentrate. Although the Statistisches Bundesamt has since 1996 made a distinction betweenpeople who are employed and those who are on temporary leave (including child-rearing leave), this distinction is rarely included in calculations of the female em-ployment rate. If no account is taken of temporary leave, then 48% of mothers withat least one child under three are in paid employment. If those on leave are excluded,the employment rate sinks to 30% (all figures taken from Beckmann, 2003, p. 6).8. Other research (Buchel & SpieX, 2002; Ondrich, SpieX, & Yang, 2002;

Beckmann & Engelbrech, 2001) has shown that highly qualified women and thosewho grew up in East Germany were more likely than other women to return to workbefore the end of their leave entitlement.9. Not only did the women train in this occupation, they also worked in it. After

completing training, 85% of the women worked full-time in this occupation.10. The normality assumptions can have particularly dramatic effects. In the

1960s a norm of celibacy for nurses was still prevalent, which at least partiallyaccounts for the high proportion of nurses without children in this cohort (33%)compared to the subsequent one (19%). Similarly, Cremer (1984) showed how theexpectation that hairdressers are young and attractive made it increasingly difficultfor mature women to remain in the occupation.11. Analyses of large data sets with sophisticated methods run the danger of de-

generating into what Hartmut Esser termed ‘variable sociology’ (Esser, 1996). Althoughthe variation between the variables may be statistically explained, the processes in thelife courses that generated them are reduced to possibly misleading causal assumptions.


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PART III:

BIOGRAPHICAL

RECONSTRUCTION

195


196

LIFE COURSE TRANSITIONS AND

SOCIAL IDENTITY CHANGE

Nicholas Emler

The thesis to be developed in this chapter is that transitions in the life course

correspond to a particular kind of category change, namely shifts in social

identity. It is therefore built upon a hitherto largely unrepresented discipline

in the study of the life course, social psychology. The utility of a perspective

that places social identity at the centre of the life course is that it draws

attention to the kinds of events and circumstances associated with, and to

some degree responsible for, life course transitions; it helps make sense of

the consequences or effects of transitions; it directs attention to questions

previously relatively neglected in the life course literature and it has the

capacity to link the approaches of different disciplines to the analysis of the

life course.

The chapter will unfold as follows. First, the traditional view of transition

within developmental psychology will be briefly set out as a counterpoint to

the view recommended here. The concept of social identity will be elabo-

rated, noting the manner in which it is currently constructed in social

psychology, particularly in Social Identity Theory (SIT) and Self-

Categorisation Theory, and then emphasising what is believed to be the

key quality of social identity, the manner in which it connects individuals to

their social worlds. Discussion will then turn to the psychological elements

and processes in identity formation, taking the example of political identity.





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197

Finally, the chapter considers transition processes and associated changes in

social relations.

PROTOTYPICAL TRANSITIONS: THE LESSONS

FROM COGNITIVE DEVELOPMENT

The butterfly provides one of the more extreme and clear-cut examples of a

life cycle marked by transitions. Periods spent successively as a caterpillar, a

chrysalis and a butterfly are separated by quite rapid changes between these

states. This life cycle would seem therefore to capture the essential features

of transitions, namely that they are rapid, qualitative changes intervening

between longer periods of relative stability. Human life cycles contain noth-

ing resembling the dramatic physical changes that result in butterflies. Un-

questionably, however, they do involve changes, and at least some of the

changes appear to be qualitative. But are these changes also transitions? An

influential prototype for this kind of change has been the Piagetian model of

cognitive development (e.g., Piaget, 1952).

Until Piaget’s work found a wider audience in the Anglophone world (cf.

Flavell, 1963), two models dominated thinking about psychological devel-

opment. One had its roots in psychoanalysis, a perspective that represented

personality development as a sequence of qualitatively distinct stages. The

other originated in learning theory and regarded development quite differ-

ently. From the perspective of this model, change was considered to be

quantitative, not qualitative, and was taken to be gradual. Piaget’s ideas

were clearly closer to psychoanalysis in one respect; he described develop-

mental change as a sequence of qualitatively distinct stages, not as gradual

quantitative change. But in other important respects it was quite distinct.

Piaget provided a very detailed formal description of each stage, and his

descriptions appeared to be empirically verifiable (Flavell, 1963).

The power and persuasiveness of Piaget’s account lent popularity to other

stage models of psychological development, including models that addressed

the themes previously within the province of psychoanalysis. Examples in-

cluded Marcia’s (1966) reworking of Erikson’s (1950) stage theory of iden-

tity development and Loevinger’s (1976) analysis of ego development. But

one of the most influential stage models to follow the route pioneered by

Piaget was Kohlberg’s (1976) theory of moral development. It drew closely

on Piaget’s cognitive developmental theory in numerous respects. It adopted

his proposal that cognitive functioning takes the form of structural wholes,

NICHOLAS EMLER198

integrated systems of thinking or generalised strategies for relating to the

world, that each possess a degree of internal equilibrium. It also regarded

the general cognitive stages identified by Piaget as the foundations for cor-

responding general stages of moral reasoning. But of particular interest here

is the account Kohlberg provided of stage transitions.

Stage transitions, Kohlberg proposed, are qualitative changes in cognitive

structure precipitated by cognitive conflict or disequilibrium. The conflict

might be largely internal in origin – for example, mutually contradictory

conclusions generated by the same reasoning system – or external in origin –

for example, arising from difficulties of assimilating new experiences within

existing cognitive structures or of solving newly encountered problems

within existing frameworks. The critical feature of the transition points or

stage changes for Kohlberg was that they were inherently unstable and

unable to endure without resolution. The resolution would be the construc-

tion of a new equilibrium.

Kohlberg’s theory has been criticised on numerous counts. The criticisms

have included challenges to most of his central claims about stages in de-

velopment of moral reasoning, namely the claims that these stages are or-

dered in a progressive, universal, invariant and irreversible sequence. I am

less concerned here with the force of these challenges than with the appro-

priateness of his neo-Piagetian concept of transition as applied to develop-

mental change. It has turned out that empirically it is difficult if not

impossible to pin down a transition point in the sense of a relatively limited

period during which one form of cognitive functioning is being replaced by

another. Moreover, this proves to be the case for the kinds of cognitive

changes first described by Piaget, for example the change from pre-

operational to concretely operational thinking as well as the developmental

shifts in moral reasoning anticipated by Kohlberg, for example the change

from pre-conventional to conventional moral reasoning. Finding clear cases

of short, unstable transitional phases in the data has proved to be difficult.

In contrast, with respect to moral reasoning, Kohlberg’s own longitudinal

data (Colby & Kohlberg, 1987) indicate that what should in theory be a

transitional condition, one in which forms of moral reasoning correspond-

ing to adjacent stages are used concurrently, is actually both more common

and more enduring than the stable states – those using just one form of

moral reasoning or the other – between which it is supposed to intervene

only briefly.

In the case of both Piaget’s and Kohlberg’s stages the changes involved do

appear to be qualitative; in each case there is a category change in cognitive

functioning. But in the concept of developmental stage both Piaget and

Life Course Transitions and Social Identity Change 199

Kohlberg combine the proposals that psychological development involves

category changes and that category changes have the characteristics of

transitions; they are relatively abrupt shifts intervening between periods of

relative stability and continuity. This model of stages does not appear to be

verifiable in the cognitive domain. Are there other kinds of category change

during the life course that do have the properties to qualify them as tran-

sitions? A promising class of candidates would appear to be provided by

changes in social identity.

SOCIAL IDENTITY

Over the past 30 years social psychology’s thinking about social identity has

been dominated by a particular point of view, at least within Europe. This is

the view first articulated in detail by Henri Tajfel (1978) as Social Identity

Theory (SIT). Tajfel’s core idea, now widely accepted in social psychology

(see Brown, 1986), is that in essence people’s subjective sense of who they are

is to an important degree determined by the manner in which they define

themselves socially. More specifically, what matters is which social catego-

ries form part of their self-definition.

This argument grew out of research on prejudice and its connection to

stereotyping. Tajfel saw that stereotyping is a process of social categorisa-

tion; it involves assigning other people to categories and regarding members

of a common category as if they were qualitatively distinct from people

outside the category. In other words, categories have boundaries and at the

boundaries there are discontinuities. Tajfel (1969) drew on his work in cog-

nitive psychology that predicted cognitive and perceptual effects of catego-

risation; the boundaries can be artificially created by perceiving greater

discontinuities across imposed category boundaries and greater similarities

within categories than exist in reality – processes also referred to as, re-

spectively, contrast and assimilation, or sharpening and levelling. Just as

there is no objective discontinuity in the electromagnetic spectrum between

the areas conventionally labelled ‘green’ and ‘blue’ so also there is no ob-

jective discontinuity between people who are categorised, respectively, as

‘black’ and ‘white’. But social categorisation can only operate as an effective

way of organising and simplifying experience of the social world by per-

ceiving it as if this was the case.

Thinking about the link between stereotyping as social categorisation on

the one hand and prejudice on the other, led inevitably to thinking about

self-categorisation. It was recognised that various ways of categorising

NICHOLAS EMLER200

social objects – other people – tend to correspond, such that those who are

categorised together descriptively are also categorised together evaluatively

or emotionally – one feels similarly about them – and categorised together

behaviourally – one treats them similarly (Doise, 1978). But why should one

feel negative about particular categories and positive about others, and why

should one discriminate against the former and favour the latter? One very

strong reason for making the evaluative/emotional and behavioural dis-

tinctions in a particular direction is that any way of categorising the social

world that is comprehensive or exhaustive must include a category to which

the self belongs. And it would be natural to regard this category as better

than others, to feel more positive about it than others, to regard it as more

worthy than others and, when the opportunity arises, to treat it more fa-

vourably than others.

It soon became apparent that matters were not this simple. Among

the problems are that people do not invariably feel positive about the

social categories they inhabit (Giles & Powesland, 1975). Nor is it clear, as

was originally suggested, that in emphasising both the categorical distinc-

tiveness and superiority of their own categories people are primarily driven

by the desire to see themselves positively, to defend their own self-esteem

(Rubin & Hewstone, 1998). But for the present discussion this is less sig-

nificant than the fact that self-categorisations and social identifications of

the self became of concern in social psychology. In effect, the focus of

theorising and research shifted to recognition that social categorisation

shapes how people think about themselves and not just how they think

about others.

The significance of self-categorisations has been explored in detail by one

of Tajfel’s students, John Turner, and systematised as Self-Categorisation

Theory (SCT; Turner, 1987). SCT incorporates several key ideas. One is that

any individual potentially has access to multiple self-categorisations, such

that from situation to situation the salience and therefore the influence of

any one of these can vary. Another is that self-categorisation has a wide

range of consequences. The particular category that is salient at any moment

will shape affiliation and communication patterns, sensitivity to sources of

influence, choices and judgments, accounts given and opinions expressed.

Perhaps the most interesting propositions of SCT, however, are that such

categorisations are organised hierarchically and that self-categorisations at

different hierarchical levels are mutually exclusive. One may categorise

oneself at the highest level of abstraction, perhaps as a living entity and in

this respect like all other members of this category, at the lowest level that is

to say as a unique individual distinct from all others, or at some level in


between. The levels in between are occupied by ‘‘social categories’’ – Italian,

catholic, socialist, female, student, chemist, etc. – but Turner’s point is that

one cannot simultaneously think and behave in terms of categories at dif-

ferent levels of the hierarchy. And most attention has been given to the

personal and social levels of categorisation as, necessarily, alternative ways

of thinking about the self.

The origins, particularly of SIT, in the study of racism and prejudice, have

been reflected in a research focus on ethnic and national identities. However,

both SIT and SCT have aspired to be generic accounts of social identity, in

principle applicable to social identity as such and therefore to any potential

form of social identity. The minimal group paradigm (Tajfel, Flament,

Billig, & Bundy, 1971) reflected this general aspiration. This paradigm cre-

ated conditions in which all meaning is stripped away from social categories

except the one quality they require for them to provide social identities,

namely that each is a categorisation shared by two or more people (they

divide a population into mutually exclusive categories). But SIT, and also

SCT have stimulated research in which a wide variety of real social identities

have been studied, including identities based on work-groups (Hennessy &

West, 1999), occupations (Skevington, 1981; Marson, 2001), enrolment in

different university courses (Reicher, 1984), support for different political

parties (Abrams, 1994), and allegiance to different football clubs (Platow

et al., 1999). Nonetheless, these different kinds of identities have more often

been studied as opportunities to validate general principles by demonstrat-

ing replication across different cases. They have less often been examined to

identify significant differences among kinds of social identities. I shall argue

that there are some such differences and that they are important, partic-

ularly from a life course perspective.

Finally, something should be said here about the relation between the

concepts of social identity and social role. In particular, there is a question

as to whether the former concept adds anything not already provided by the

traditional sociological analysis of roles. According to one view, social cat-

egories and roles are actually alternative bases for structuring interactions

(Mitchell, 1969). I suggest, however, that they emphasise different facets of

the ways people relate to one another. The role concept, with its roots in

drama, focuses on the manner in which the responsibility for tasks is dis-

tributed among positions within a shared structure. The emphasis therefore

is upon integration of different activities. This approach draws our attention

to the manner in which roles relate to and complement other roles – role sets

being constituted by organisational structures; human organisations have,

indeed, been defined as a system of roles (Katz & Kahn, 1978).

NICHOLAS EMLER202

There are three obvious ways in which a social identity perspective differs.

First, it emphasises differentiation rather than integration; its focus, as pre-

viously noted, is on the processes that reinforce similarities within categories

and differences between categories, on boundaries rather than on links.

Second, it emphasises who people are seen to be and feel themselves to be in

contrast to the role perspective emphasis on the tasks people perform. In

these two respects the two concepts are somewhat complementary. Third,

however, social identity appears to be a more inclusive concept. All social

roles create categories of people and thus social identities, but many cate-

gories do not entail roles in a shared social organisational structure. Think,

for example, of social identities based on religion, nationality or ethnicity. As

to whether social identity or social role is the more useful concept in studying

the life course; the answer I think is that both have a contribution to make.

This chapter focuses on the hitherto more neglected contribution of the two.

The paradigm case in the social psychology of social identity remains

ethnic identity, an identity that is normally relatively fixed over the life

course. And perhaps for this reason, analysis of change in social identity –

which naturally arises as a central question for a life course approach – has

not been extensive within these theoretical traditions. The principle excep-

tion has been Tajfel’s own examination of social mobility (Tajfel, 1978). But

mobility was proposed as a defensive manoeuvre, undertaken only rarely

and then to protect self-worth by escaping from a negative identity. The

absence of compelling evidence that membership of low prestige social cat-

egories is associated with lower self-esteem (Emler, 2001) argues against this

motivation. Within the life course frame social identity change is normative,

not exceptional. The challenge is to explain the process of change.

CORE SOCIAL IDENTITIES

Self-categorisation theory emphasises the situational specificity of social

identification, which is to say categories and category contrasts that are

momentarily salient for the individual social actor. Indeed, Turner (1987)

notes that social categorisations can be spontaneous or emergent construc-

tions within a particular situation, the so-called minimal group being just

such a situation; social identities are therefore not confined to classifications

that are culturally provided or built into the social structure of a society.

From the perspective of life course analysis, however, social identities that

both dominate or characterise entire periods of life and that are widely

recognised and rooted within the culture are of particular interest.


But is it really the case that some social identities are more dominant or

central, chronically more salient? One kind of research that might decide this

would sample individuals’ identity salience across time, much in the way

that experience sampling methods have been used to determine the prev-

alence of different mood states (cf. Czikszentmihalyi & Larson, 1987), but

such research is yet to be done. Indeed, it is more generally true that to date

there has been almost no longitudinal research focused specifically on social

identity, so that the evidence available to us is cross-sectional – comparisons

of people at different ages or life stages. There are, however, other kinds of

indications that some social identities are reliably more central or significant

in people’s lives. One such indication comes from research on the strength of

different kinds of identification in the world of work. Marson (2001) showed

that of the three kinds of identification in this domain, respectively with a

work team, with an employing organisation and with an occupation or

profession, the third consistently emerged as by far the strongest across a

variety of populations.

Second, as Fiske (1998) points out, some categorisations are more reg-

ularly important because they are visually highly salient and therefore so-

cially functional; they are immediately available to structure social

interactions. Fiske’s top three categories on this basis are race, gender and

age. However, other categorisations can be and often are made chronically

visually salient by manipulating appearance. This can be done through

conventionalised styles of grooming and adornment and even through bod-

ily movement, as Mauss (1935) observed many years ago. But these options

are usually combined with an even more visually powerful marker, dress.

The alternative categories with which young people have identified – hippy,

skinhead, goth, punk and so on – are signalled by combinations of dress

codes and bodily adornments, as are many other significant social catego-

risations based on ethnicity, religion and even class and occupation. The

point is that such visual markers of social identity lend cross-situational

durability, and therefore importance, to these identities because they cannot

rapidly be changed. And the rapid changes in appearance that are possible –

shaving the head, acquiring a tattoo – are only significant if they cannot be

equally rapidly reversed.

Another source of evidence comes from research on the manner in which

connections are anticipated in networks of acquaintances. Milgram’s (1967)

studies of the ‘‘small-world’’ phenomenon led to a research showing that

people are surprisingly good at working out how they can efficiently reach

others they do not yet know through the intermediary of personal ac-

quaintances (Klineberg, 2000). It appears that this facility is based on the

NICHOLAS EMLER204

fact that acquaintances have multiple social identities; they can be simul-

taneously classified in several different ways. However, when people

undertake this task they typically use only a small number of these poten-

tial classifications, and usually only two of them. Those most commonly

used are based, respectively, on occupation and geography (Killworth &

Barnard, 1978).

This last example underlines another feature of the social identities that is

likely to be most important over the life course: these are the principle

categories applied to people by their own acquaintances. Social identities are

therefore social in two distinct respects. As categories they are socially de-

fined and the most relevant categories will be widely recognised within a

culture. As applied to particular individuals, however, these identities are

validated by others. A person may aspire to a particular identity but unless

this aspiration is acknowledged by at least some significant others it remains

no more than a private desire. Social identities are viable bases for relating

to others only if at some level they have been negotiated with and accepted

by a relevant audience, an argument we have made on the basis of our

studies of delinquent and non-delinquent identities (Emler & Reicher, 1995);

social identities assume reality and become consequential when they are

performed before an audience and when the audience confirms the authen-

ticity of the performance (cf. also Emler, 1990).

If identities reflect how others define us, they might nonetheless be re-

lationship-specific. This was the essence of William James’s famous decla-

ration that we have as many selves as people who know us, the implication

being that identities are entirely relationship-specific. Though at some level

this might be true, the degree to which it applies is probably trivial. It would

be very odd if people who know you well did not agree whether you were a

mother, a Catholic, of Irish descent, a civil servant and so on. It might be

countered that these are not the essence of the self that others know, but the

response is that they and other such categories do constitute the social self.

There will be, in addition, a history to each relationship that is its own and a

judgment by the other on this basis as to one’s personality. But such judg-

ments are also not, as a matter of evidence, relationship-specific; our ac-

quaintances tend to agree about our personality (Kenrick & Stringfield,

1980), and the better they know us the more they agree (Kenny, 1994). Some

measure of agreement is additionally generated by the fact that our ac-

quaintances also know each other and share with each other their views of

us.

Up to this point the importance of social identities to everyday life can be

summarised as follows: our social identities shape the character and content


of our relations with others. Perhaps therefore an indicator of the centrality

of an identity is the scope of such effects – how many details of our re-

lationships are affected, and with how many of the others we know and

interact with.

Social identities are likely to shape two further details of our connections

to the social world: who we know and who we encounter on a daily basis. In

the first case, the effect is that we will be personally acquainted with more

people who share a social identity with us than will people who do not share

the social identity with us. University students will know more members of

this category (and mothers will know more mothers, socialists more social-

ists, Glaswegians more Glaswegians, delinquents more delinquents, married

people more married people) than will people who are not university

students etc. This, of course is the basis for the ‘‘small-world’’ facility de-

scribed by Killworth and Bernard (1978).

Additionally, for students and for members of some other categories,

many and perhaps the majority of their daily encounters are with other

members of the same category. But although these two features – who else

one knows and who else one encounters most regularly – are sometimes

strongly related, they are not the same. Take the extreme case in which a

priest or a doctor is one of a kind in a small, rural community. In each case

their daily encounters will be strongly shaped by their vocation, many of the

priest’s encounters are with other community residents as his parishioners,

and the doctor’s with residents as patients. Other members of their own

respective vocational categories will not dominate their array of daily social

contacts. But both will have personal acquaintances who do belong to their

own social category at a higher rate than will other members of their com-

munity; the doctor will know more doctors than will his or her patients, the

priest will know more priests than will his parishioners. In other words, part

of what it means to belong to a social category is that one will be personally

acquainted with other members of the category at a higher rate than the

base rate for the population as a whole.

In a series of studies we have collected data on people’s patterns of rou-

tine social contacts (e.g., Emler, 1990; Emler & McNamara, 1996). These

data have been derived from a variety of groups, including adolescents in

high school, university students, young people in employment, and adults

working as managers, in effect groups of people belonging to different social

categories. The data indicated that various features of contact patterns are

associated with category membership. This point is illustrated in Fig. 1 with

data (from Emler, 2000) on the social contact patterns of young people in

this case all at the same point in the life course but occupying one of four

NICHOLAS EMLER206

distinct categories. These data do not give us the social identities of the

contacts, but it is a fair guess that many are members of the same social

categories as the respondent – university students in the case of the first

group, vocational college students in the case of the second, work colleagues

in the case of the third. The guess is supported by the common locations for

encounters in each group, respectively, on the university campus, at the

college and in the workplace. Something else that these data reveal is that

alternative social category memberships can be associated with dramatically

different rates of social contact;for example, compare the unemployed group

with others.

To summarise, social identities connect individual to social worlds in the

following respects. First, individuals are identified by significant others with

particular social categories and on the basis of this others have particular

expectations about how an individual will and should behave, about who

else that person knows and about how that person should be treated. In

other words, social identities structure interactions. Second, social identities

are reflected in the categories of people an individual is acquainted with.

This has consequences for the resources to which they have access and their

value to others. Third, social identities are linked to patterns of routine

social contact; often, but not always, people will tend to have more regular

contact with other members of the same social category than will those who

0

2

4

6

8

10

12

14

16

18

20

Close Fr Other Fr Cas Aqu Formal Stranger

Univ

Coll

Employ

Unemp

Fig. 1. Contact Patterns: Number of Different People Encountered in

Each Relationship Category (Over 7 days).


do not belong to that category. This has consequences for, among other

things, the range of ideas, opinions and influences to which individuals are

most exposed.

CHANGING SOCIAL IDENTITY

If the above summary is appropriate, then it has important implications for

transitions in social identity. In effect, changes in social identity require

changes in social relationships. Moreover, the foregoing analysis implies two

kinds of change in social relationships. The first is change in how you are

defined, and therefore treated by the people who already know you – the

substance and character of your interactions with them. The second is

change in who you know and with whom you regularly associate.

Superficially, it might appear that changes of these latter kinds would

not always and invariably occur. Think of a child who grows to adulthood,

gets married and becomes a parent in a small agricultural community. At

each of these transitions there might be small adjustments in the patterns

of daily contact, for example more with the new spouse and less with

other opposite sex peers – but there will be little change in who this indi-

vidual knows. Childhood friends and playmates are likely to go through

similar transitions at similar ages, and so become adult neighbours and

acquaintances.

The problem with this scenario is that entirely closed human communities

in which the only turnover in membership arises from births and deaths do

not and never have existed. On the other hand, there is undoubtedly var-

iation across time and societies in the extent of turnover in community

membership and thus variation in the continuity of any individual’s ac-

quaintances (Slater, 1968). Moreover, to the extent that emotional ties and

regular contacts persist with the same people across transitions in social

identity these would appear to provide obstacles to the process of successful

transition. Because social identities derive stability from the continuity of

relations with others, this very continuity is also a source of inertia. If the

people who know you are in the habit of thinking of you in certain terms,

treating you in particular ways, and having specific expectations about you,

they will not readily or easily switch to thinking, behaving and evaluating in

quite different ways. The mother who has difficulty adjusting to the change

in status of her beloved son from child to adult is only a more extreme

example of a commonplace phenomenon. It may be that the various rituals

of transition – wedding services, baptisms, graduation ceremonies,

NICHOLAS EMLER208

21st birthday parties, coronations, retirement celebrations – are social de-

vices to counteract such inertia. In these rituals, communities gather and

publicly acknowledge a movement of one or more of their number from one

social category to another.

Later, I will reconsider these transition rituals together with the contem-

porary significance of changes in acquaintance and contact patterns. But if a

change in social identity is a change in the way the individual is connected to

the social world, there are nonetheless other, more internal and psycholog-

ical aspects to the process of social identity change. This can be illustrated

with the case of political identity.

THE FORMATION OF POLITICAL IDENTITY

The political life of societies tends to be played out as a competition between

groups of people who have adopted differing political identities. For a long

time political psychology was dominated by questions about the determi-

nants of this choice, interpreted as one of positioning along a left–right

ideological dimension: why do some people end up aligned with the political

left and others with the political centre or right? However, it gradually

emerged that an additional and perhaps more significant question existed:

why do people become politically aligned at all? This question was pro-

moted by the observation that many adults appear to have no political

leanings (Converse, 1964). Subsequently, evidence for a wide variety of

variations in political involvement emerged, including variations in the ex-

tent of political participation, interest in politics, opinion stability, political

knowledge, attention to political events and so on. Research in which these

dimensions of variability have been concurrently assessed has indicated that

they are strongly interrelated and are apparently manifestations of a com-

mon latent variable. In one such study, Nie, Junn and Stehlik-Barry (1996)

labelled this variable ‘political engagement’.

Another interpretation is possible, however. Identity formation is a proc-

ess in which different elements are related in a causal sequence. In the case of

political identity, the following sequence might be proposed (cf. Emler,

2002). First, one needs to have some interest in politics, and this is a nec-

essary condition for taking a second step, namely, paying attention to po-

litical information and events. Such attentiveness allows the individual to

accumulate knowledge of the political world. This individual can then begin

to form judgments or opinions on the basis of this knowledge, and


subsequently to organise these opinions into coherent political positions.

Finally, an established political position provides the foundation for ide-

ologically consistent political action.

We have made a preliminary test (Emler, Romney, & Bynner, 2004) of a

sequential model of political identity formation by drawing on data from a

large sample of 16–20 -year-olds (Bynner, Romney & Emler, 2003). This test

supports a slightly modified sequence, which begins with political interest

leading to attentiveness. This later, in turn, precedes responsiveness defined

as a positive attitude towards politics. Responsiveness in its turn leads to

action; in the data set action was assessed in terms of attending political

meetings, taking part in demonstrations, etc. The final three steps in the

sequence are from action to voting intention, and thence to opinionation,

this last step reflecting the extent to which individuals have clear opinions on

political questions. This was an imperfect test given that it was not based on

data collected specifically to test the model. In particular, there was no

measure of political knowledge. However, the relevance of this example is to

make the point that a developmental model of identity formation or change

is testable, and can provide a basis for assessing progress towards a new

identity.

I would also argue that the four kinds of psychological elements that

appear to be involved in the construction of a political identity, namely,

motivational, cognitive, attitudinal and behavioural elements, will be found

in all significant social identities, and particularly in those that dominate

and define phases in the life course. This tells us that social identity change is

a process requiring time because it requires a sequence of interconnected

psychological changes.

Having considered the psychological elements of identity formation and

change, it is important to recognise that the psychological changes are

also socially supported. In the case of political identity, motivation or in-

terest will be encouraged or discouraged by acquaintances. Palmonari,

Pombeni, & Kirchler (1992) give the example of young people belonging to

friendship groups in which interest in politics violates group norms. Friends

and acquaintances may be sources of political information but are likely to

play an even more important part in opinion formation and attitude or-

ganisation, providing opportunities to rehearse positions and check their

social meaning (Emler, 1990). Finally, others will be role models for, and

mobilisers to, action. Progress in the formation of political identity, there-

fore, will depend on access to others willing and able to provide these sup-

porting roles. Parallel social processes are likely to be at work in other kinds

of social identity change.

NICHOLAS EMLER210

SOCIAL IDENTITY CHANGE AND

THE PROCESS OF TRANSITION

There are features of the change in political identity, from unaligned to

aligned, that would seem to make it quite an atypical example of the identity

changes characterising the life course. In particular, there is no abrupt

transition in identity. On the contrary, the change is characteristically grad-

ual. But is it really the case that the life course is periodically punctuated by

rapid shifts in social identity? This brings us back to the concept of tran-

sition as a change that is abrupt as well as qualitative.

Major life course transitions are often represented as specific events –

puberty, graduation from university, the first day at work, a wedding (or

divorce), birth of a first child, retirement day. But, with the exception of

childbirth, there are common examples of more extended transitions cor-

responding to each of these cases. It is now common for many couples

to move gradually towards shared domestic arrangements long before

marriage and in many cases the relationship is never formalised as a legal

marriage. The move into work is frequently drawn out through work ex-

perience and periods of training or apprenticeship. At the other end of their

working lives, it is not uncommon for people to go part-time long before

they finally cease working for an income. In Finland, a high proportion of

students never terminate their studies with a completed qualification; instead

they become progressively more part-time as students, spending corre-

spondingly more time working for an income, and so pass gradually rather

than abruptly from the status of a student to that of a worker.

The most conspicuous case of protracted transition, however, is that from

childhood to adulthood, a process to which the very term ‘‘extended tran-

sition’’ was first regularly applied. In contemporary societies there is no

specific event to mark this transition. Instead, there is a progressive accu-

mulation of legal rights at different ages, together with gradual financial and

domestic independence from parents, which may not be completed until the

late 20s or even beyond (Jones & Wallace, 1992).

These various extended transitions have both advantages and disadvan-

tages. In so far as new social identities require new knowledge, attitudes and

behavioural repertoires, the extended time scale provides more opportunity

to develop and integrate these. The major disadvantage is in achieving social

recognition of the new identity.

To see how this disadvantage might be overcome, let us return to the

process by which new social identities are established between the individuals

who are to adopt them and the social worlds in which they dwell. How is this


social world led to accept an individual’s movement into a new identity? As

already noted, rituals of transition provide devices for securing collective and

public consent to new identities. But, as already noted, both the prevalence

and the significance of many of these appear to be on the decline. This

decline can be linked to a concurrent rise in resort to two other options,

either to change one’s appearance or to change the people one knows.

Formal changes in appearance are, like rituals of transition, currently

becoming less commonplace. In Britain most children used to go to school

in uniforms, and then changed or abandoned uniforms as their age-based

identities changed, but these practices are in decline. Manner of dress also

signifies certain religious identities, and thus their adoption and abandon-

ment. Practitioners of a few vocations still signal their identities by routinely

wearing the appropriate apparel. But occupation-linked uniforms now more

often signal a role only temporarily occupied, a time- and place-specific

performance. On the other hand, one might speculate that when teenagers

are drawn to sub-cultures with highly distinctive dress codes this will be

filling the gap left by the transition rituals that would in former times have

marked a change in identity from child to adult.

What of the other option, changing the people one knows? This is most

readily accomplished by a radical alteration in the patterns of daily contact.

In effect, we interact with the people whose paths cross ours in the course of

our regular daily activities (Emler, 2000). This might seem a trivial point but

it is quite fundamental. Social contact is generated primarily by routines

that take people into particular places to do particular things at particular

times. Contact patterns are therefore altered by changes in these routines.

Birth of a first child usually forces a substantial shift in a woman’s routine

(but also usually has rather less effect on the father’s routine).

The change produced by childbirth in a mother’s routine arises from the

fact that she must commit her time in a new way, and spend more of it in a

particular setting. This, namely relocation in physical space, is the principle

way in which routines, more generally, are altered. There are many examples

of this effect – going to school for the first time, moving from elementary to

high school (and, still a common pattern in Britain going to a residential or

a boarding school), leaving home to go to university, leaving the parental

home to set up one’s own, starting a new job, doing military service, moving

on retirement to a warmer climate. Each of these physical displacements has

the effect of altering patterns of routine social contact, and therefore each

potentially supports a shift in social identity.

Equally evident is that there are cultural variations in common practice

that may reflect the problems a particular economy poses for identity

NICHOLAS EMLER212

change. Children growing up on a Kibbutz have been expected to work on

the Kibbutz as adults but have also been expected at the end of their ed-

ucation to leave for a year and travel (and during this period also find a

spouse). And there are changes in practice over time. In Britain, the tra-

dition of leaving home to attend university is in the decline as rising costs

force more students to attend a local university so that they can continue

living at home. But this trend has been accompanied by another that pro-

duces a break in routine and a discontinuity in relationships, the ‘‘gap year’’

in which travelling abroad intervenes between high school and university.

It is also evident that physical displacement is not a purely modern so-

lution. Some tribal societies have created discontinuity of association in

their otherwise settled communities by physically segregating the sexes for a

time at puberty (Goode, 1959). In the 19th century it was the fashion for

young Englishmen of aristocratic background to depart on a European

tour, and in the 20th century for the females to be sent off to ‘‘finishing

schools’’ in Switzerland. More generally, leaving home at the end of child-

hood to find fame and fortune as well as to find an adult identity, has long

been a theme of folklore and fairytales. Indeed, life as a journey is an almost

universal metaphor, but perhaps not so metaphorical; real journeys are

involved.

At the conclusion of this particular journey, I wish to argue that it has

shown that transitions in the life course as changes in social identity are

more likely to be gradual than abrupt, for two kinds of reasons. First,

identity change requires a number of psychological changes that take time to

build. Second, social identities derive resistance to change from the rela-

tionships with others in which they are embedded and through which they

are confirmed. But a common response to this inertia is some displacement

in physical space, supporting both the formation of new relationships on a

new basis and a return to the old ones from the vantage of a new category.

One hope is that the future will bring the kind of longitudinal evidence

needed to move these ideas about social identity change in the life course

from the realm of speculation to that of empirically grounded observation.

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216

THE IMPACT OF PERSONALITY

AND LIVING CONTEXT

ON REMEMBERING

BIOGRAPHICAL TRANSITIONS

Pasqualina Perrig-Chiello and Walter J. Perrig

1. BIOGRAPHICAL EXPERIENCES AND THEIR

IMPORTANCE FOR THE REGULATION OF

WELL-BEING

The question, as to what extent past experiences have an impact on current

well-being and future anticipations, has provided an important, but con-

troversial, research topic since the very beginning of psychology as a dis-

cipline in its own right. Some investigators have emphasized the importance

of past biographical experiences, claiming that, at any given time, individ-

uals are very much the product of their own life history quite apart from

external, situational demands, opportunities and barriers (Freud, 1917;

Erikson, 1959; McAdams, 1993). Others have pointed to a lack of reliable

empirical evidence on this matter, owing to the largely retrospective char-

acter of most of the data (Rutter, 1996).

Recent advances in life-span developmental psychology have meanwhile

brought increasing empirical insight and rigor to this crucial research ques-

tion. It has been suggested that individual lives can be characterized as a





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10008-2

217

series of interrelated transitions. Transitions define points in the human life

cycle when roles are transformed, redefined and left behind for new roles

(e.g. starting school, experiencing puberty, starting work, leaving home,

getting married, having children, retirement, etc.). These changes involve

and give shape and direction to various aspects of a person’s life (Mercer,

Nichols, & Doyle, 1989; Sugarman, 2001).

It has been argued that early transitions can have lasting effects on sub-

sequent transitions, even after many years and decades have passed (Elder,

1998). Some studies have shown a wide range of effects in later life following

the experience of specific transitions in childhood (Wadsworth, Maclean,

Kuh, & Rodgers, 1990; Wertlieb, 1997) and adolescence. For example,

psychological functioning during puberty and adolescence was significantly

correlated with that in old age (Vaillant, 1990; Clausen, 1991). Ryff and

Heidrich (1997) showed that reported normative early life events and tran-

sitions were significant predictors for multiple aspects of present and future

well-being across different age groups.

Such findings lead to questions about the underlying mechanisms re-

sponsible for such a relationship. For example, can a biographical transition

directly affect, or even give rise to, a specific developmental outcome in later

life course? Could there also be a third, mediating variable behind the ob-

served correlation? Or, is it the case that some kind of backward inference,

that is, for example recollected (or even reconstructed) mental projections

from the present into the past can account for the data? This chapter is

devoted to these questions and mainly elaborates on those memory proc-

esses that are related to autobiographical experience and its role on reg-

ulation of well-being (see also McAdams contribution in this volume).

2. TRANSITIONS AND LIFE EVENTS: OBJECTIVE

FACTS VERSUS SUBJECTIVE RECONSTRUCTIONS

Despite the persuasiveness of the relationship between earlier transitions

and later developmental outcomes, the theoretical basis of these findings is

far beyond clear. Because these findings are mainly based on retrospective

reports, the question cannot easily be answered, whether a past transition

per se has a long-term effect on later life, or whether it is rather the sub-

jective post-hoc interpretation that matters (Rutter, 1996).

From a memory theoretical point of view, subjective reports of the

past are, like all autobiographical memories, the products of personal

PASQUALINA PERRIG-CHIELLO AND WALTER J. PERRIG218

reconstruction. For example, if a relationship is found between the remem-

bered past and later life events, it could well be that the memorized emo-

tional quality of the previous events accounts for later well-being and not

the event per se (Schacter, 1996; Schwarz & Strack, 1999; Keyes & Ryff,

1999). In memory research there is abundant evidence that reports of the

past are not objective records of events but constructions that are biased by

multiple sources. Remembering embraces memory traces, amalgamated

with actual perceived information and contextual constraints. These are all

subject to current decision-making processes that can be very subtle, re-

sulting in statements with varying accuracy and giving rise to subjective

(rather than objective) truth values (Tulving, 1983; Johnson, Hashtroudi, &

Lindsay, 1993; Rubin & Berntsen, 2003). Furthermore, recalling biograph-

ical episodes not only includes re-experiencing the past, but also the factual

retrieval of autobiographical knowledge that e.g. might have been reported

by others.

There is good evidence in the literature for a remember/know distinction

in memory (Conway, 1987; Gardiner & Java, 1990; Rubin, 1986) even down

at the neuropsychological level (Wheeler, Stuss, & Tulving, 1997). Also, it is

generally accepted that autobiographical knowledge is stored and organized

within a hierarchical framework (Conway & Rubin, 1993). A lifetime period

is the most general, most abstract and most inclusive type of knowledge, and

denotes typically units of years. General events (at the next lower hierar-

chical level) represent common or frequently experienced episodes that are

amalgamated and consolidated in memory, giving rise to a kind of basic, but

personalized, knowledge store (Bartlett, 1932/1993). Specific, single-event

knowledge (at the lowest hierarchical level) is particularly salient, and gives

rise to unique memory traces that appear to be stored separately. In sub-

jective reports of past life transitions, it will never be easy to separate out

what is actually re-experienced (episodic memory) from what is generally

known – and expected (semantic memory) in any of the three types of

autobiographical memory. Neither it is clear, whether memory reports rep-

resent real facts from the past or just constructed inferences induced by the

retrieval setting. Thus, in exploring past-event reports the distinction be-

tween the more cognitive-oriented analyses of autobiographical reports and

the more experiential-oriented analyses of reminiscence is an important one

(Bluck & Alea, 2002). Memorized life transitions can thus be considered as

products of a kind of motivated forgetting or remembering serving indi-

vidual psychodynamic functions (such as equilibration of self-identity).

There are ample reports in the literature of work describing phenomena

related to constructed, reconstructed and distorted memories in general

The Impact of Personality and Living Context 219

(Ayers & Reder, 1998; Brainerd, Reyna, Wright, & Mojardin, 2003) and in

autobiographical memory research in particular (Brewer, 1988; Conway &

Rubin, 1993). There are impressive demonstrations of how misleading

information or faint feelings of familiarity can bias memory reports

(Whittlesea, 2002). Certainly, the most dramatic demonstrations of false

memories describe how participants in certain experimental studies can be-

gin to ‘‘remember’’ painful autobiographical events that were simply sug-

gested to them and which never really happened (Loftus, 2003). Even so, the

precise constructive mechanisms underlying false or distorted reports of past

events are not yet completely understood and continue to be investigated

(Conway, 1996; Bluck, 2003; Bluck & Alea, 2002).

Bartlett’s sensible analysis of remembering and his theoretical conclusions

(Bartlett, 1932/1993) help to better understand these processes. In his view,

the remembering of autobiographical events is influenced by ‘‘schematic’’

knowledge. Bartlett’s ‘‘schema’’ refers to the activation of relevant past

experience in such a way that it affects the interpretation of any presently

incoming sensory information. Although this usefully ensures the updating

of general knowledge held in memory, and helps us to respond in situations

where there is inadequate information to hand, it can sometimes lead us

astray through faulty expectations. For example, when a subject starts to

remember a complex situation, the first thing that comes to mind is often a

general impression of the whole, which is an ‘‘attitude’’ toward it. By ‘‘at-

titude’’, Bartlett referred to a complex psychological state or process, which

is very largely a matter of feeling or affect. He characterized this state as a

collection of subjective phenomena such as ‘‘yby doubt, hesitation, sur-

prise, astonishment, confidence, dislike, repulsion and so on’’ (Bartlett,

1932/1993, p. 207). Recall of the situation or event is thus a reconstruction,

made largely on the basis of this current cognitive–emotional attitude. The

general effect is that of a justification of the attitude: ‘‘This and this and this

must have occurred, in order that my present state should be what it is’’

(Bartlett, 1932/1993, p. 202).

If the topic of remembering is a biographical transition, the relevant

‘‘schema’’ for self-related knowledge will be activated and may control the

process of remembering. Here, the actual schema held by a person about

her/himself (self-concept) can be considered as his/her hierarchically organ-

ized, autobiographical knowledge that has developed over time and has

been very largely determined by immediately preceding or actual experi-

ences. Therefore, the current schemata or themes of the self allows for

constancy as well as change. Obviously, there is enormous stability in the

preservation and continuity of one’s self-identity and of personality, that is,


the complex of all the attributes that characterize a unique individual over

time. But there is also instability in the construction of one’s autobiograph-

ical memories, which are dependent on the situational living contexts of

different retrieval episodes. Predictions from this theoretical perspective can

be applied to (1) the correlation between remembered life events and actual

well-being, (2) the correlation between self-identity/personality and memory

performance and (3) the influence of particular living contexts on remem-

bering the past.

Based on the arguments set out above, we would expect firstly that any

studies describing past transitions and actual life situations by the means of

subjective reports should find significant relationships between the two. The

reason for this is twofold: On one side autobiographical schematic knowl-

edge contributes to the constancy and stability of self-identity over time

(Bluck, 2003), on the other side the constructive processes involved in ex-

periencing the actual situation as well as remembering the past are self-

serving in the regulation of the actual well-being. Actual well-being and

stability in self-identity is based on conditions of the present, but also on the

past, that serves as reference or justification for the actual situation. Such

self-serving regulatory processes do operate subtly and smoothly during

stable periods of life, but might be extreme in their effects in specific living

contexts (e.g. critical life situations or in therapeutic settings).

Our further predictions relate to the role of personality and the situational

living context in reports of past and present life events and circumstances.

By personality, we refer to the complex of attributes that characterize a

unique individual over time, which includes autobiographical knowledge,

self-perception and self-identity.

From the constancy necessary in the accumulation and integration of a

stable self, we can predict secondly that individual differences in personality

correlate with differences in type or style of cognitive as well as of emotional

processes. Because differences of personality can be measured reliably by

questionnaires, personality inventories can be used as a direct test of per-

sonality. From this it follows that we should also be able to find relation-

ships between life-transition reports and personality measures. Indeed, such

are the results of the studies we report below.

Thirdly, quite apart from personality variables, we should also be able to

demonstrate correlations between the current living context and retrospec-

tive life-transition reports. The importance of shedding light into these

processes has been pointed out by different authors (Baltes, Lindenberger, &

Staudinger, 1998; Bluck, 2003). And again, we are able to present empirical

evidence below for this prediction.


3. RELATIONSHIP BETWEEN BIOGRAPHICAL

RECOLLECTION, PERSONALITY, LIVING CONTEXT

AND WELL-BEING: THREE ILLUSTRATIVE STUDIES

ON MIDDLE AND OLD AGE

In this contribution we want to give illustrative examples on how autobi-

ographical reports of past life transitions or life events and actual well-being

are related, and how personality and specific living contexts, such as going

through a particular biographical transition like marital separation or dis-

missal from work may have an impact on autobiographical recollection. The

findings reported here are based on data from two research programs:

(1) from the ‘‘Basle Interdisciplinary Study on Ageing’’ (IDA-Study1)

focusing on old persons (Perrig–Chiello, Perrig, Stahelin, Krebs, & Ehrsam,

1996) and (2) from the study ‘‘Transitions and Life Perspectives in Middle

Age’’2 focusing persons in middle age (Perrig-Chiello, Hopflinger, Kaiser, &

Sturzenegger, 1999).

Based on the theoretical rationale presented above, we analyzed the re-

lationships between the emotional valence of autobiographical memories,

actual well-being, personality and actual living context.

We hypothesize that:

(a) There is a positive relationship between the emotional valence of ret-

rospective recollected autobiographical memories and current well-

being.

(b) This relationship is due on the one hand to inherent personality var-

iables and on the other to specific living-context factors, which mediate

the effects.

3.1. The Impact of Personality on Biographical Reports and Well-Being in

Middle and Old Age (Study 1)

3.1.1. Evidence from Research on Old Age

The interview data reported here are original and were collected through the

Basel Interdisciplinary Study on Ageing (IDA-Study) (Perrig-Chiello et al.,

1996). The aim of the IDA-Study was the examination of the cross-sectional

and longitudinal changes of health, well-being and autonomy in old age, and

their determinants. The project was specifically designed to document the

availability of physical, psychological and social resources, and to

investigate their impact on these three outcome variables (health, well-being


and autonomy). The project is a follow-up of a longitudinal study that

started in 1960 and takes into account the data collected in 1993. Participants

in the IDA-study had to be 65 years or older at the time of interview. They

were selected by random sampling from the longitudinal pool, which still

comprised 3,768 people in 1993. For the IDA-study, 442 healthy persons

aged 65–94 (309 males and 133 females, mean age: 74.95 years) agreed to

participate. Psychological assessment included:

(a) Life event inventory: A list of thematically ordered life events was pre-

sented to the participants (own health, housing condition, financial

condition, family concerns, activities, etc.) (see the appendix). For each

of these domains they were asked to indicate whether and how they had

experienced specific events within the last 10 years.

(b) Personality: In order to assess personality traits we used the two main

subscales ‘‘Extraversion’’ and ‘‘Neuroticism’’ from the Freiburger

Personlichkeits-Inventar (Fahrenberg, Hampel, & Selg, 1970, 1984) (be-

ing the most used personality inventory in German-speaking countries).

(c) Psychological well-being was assessed by means of a 9-item-scale which

taps the dimensions ‘‘satisfaction with own past’’, ‘‘purpose of life’’ and

‘‘mastery’’. This instrument meets all psychometric standards (see

Perrig-Chiello, 1996, 1997).

In order to answer the specific question about the relationship between

the number of the reported positive and negative life events, personality

variables and current well-being, we first performed correlational analyses.

Results show that the reported number of positive life events is negatively

correlated with neuroticism (R ¼ �0:132; po0.05) and positively with psy-

chological well-being (R ¼ 0:118; po0.05), whereas negative life events are

positively correlated with neuroticism (R ¼ 0:123; po0.05). In a second

step, we calculated multiple-regression analyses in order to determine the

predictive power of the reported number of positive and negative life events

on actual psychological well-being. Since in our data we had an overrep-

resentation of men, we controlled also for gender. Results show that the

number of reported negative and positive life events is indeed significant

predictor of psychological well-being, independently of gender. However, as

soon as neuroticism is introduced as additional predictor, the number of

reported life events loses its predictive power in favor of neuroticism (see

Table 1).

Even though these effects are not very large, they are reliable. Further-

more, the same phenomenon was demonstrated in another study with a

middle-aged sample, as described in the following section.


3.1.2. Evidence from Research on Middle Age

The findings reported here stem from an interdisciplinary longitudinal study

entitled ‘‘Transitions and Life Perspectives in Middle Age’’ (Perrig-Chiello

et al., 1999). The study investigated the impact of past transitions on current

well-being and on anticipation of old age. A total of 268 middle-aged per-

sons (197 women; 71 men) participated in the study (mean age ¼ 47:2years). This sample is a subsample of a larger survey study (N ¼ 1015) and

can be considered as being representative of a healthy middle-aged urban

population in Switzerland. Participants in the study completed two ques-

tionnaires (psychological well-being and personality) and were given an in-

depth interview on biographical transitions (timing and emotional valence

of transitions) (Perrig-Chiello & Hopflinger, 2001). The crucial variables

were assessed as follows:

(a) Reported biographical transitions: A list of age-normed and frequent

non-age-normed transitions was presented and participants had to in-

dicate the age at which the specific transition occurred, as well as the

emotional valence of this event as experienced at that time. The list

referred to transitions across the whole life span (from school entry,

puberty, first love through anticipation of retirement and transition to

old age).

(b) Psychological well-being was assessed by means of the same nine-item

test as in the IDA-Study mentioned previously.

(c) Personality was assessed by means of the NEO-Five Factor Inventory

(Costa & McCrae, 1985).

Table 1. Life Events and Neuroticism: Which Measure is Better as

Predicting Current Well-Being?.

Predictors of Well-Being Standard b (p)

Model 1 Number of negative life events �0.11 (0.03)

Number of positive life events 0.16 (0.001)

Gender �0.05 (n.s.)

N ¼ 301 R ¼ 0:17; R2 ¼ 0:03; p ¼ 0:004

Model 2 Neuroticism �0.256 (o0.0001)

Number of negative life events �0.02 (n.s.)

Number of positive life events 0.087 (n.s.)

Gender �0.02 (n.s.)

N ¼ 301 R ¼ 0:28; R2 ¼ 0:08; po0.0001


Regression analyses were performed to evaluate the predictive power of

emotional valence of experienced transitions along with personality varia-

bles on present psychological well-being. Age group and gender, emotional

valence of past transitions as well as personality served as predictor var-

iables. We performed hierarchical regression analyses; age group and gender

were entered as block 1 (R2 ¼ 0:05), emotional valence of transitions as

block 2 (R2 ¼ 0:13) and personality variables as block 3 (R2 ¼ 0:42). Theanalyses revealed that, in particular, emotional valence of one transition,

namely puberty, was a significant predictor of actual psychological well-

being (b ¼ 0:18; po0.05). However, as soon as personality variables were

entered, psychological well-being was best predicted by neuroticism

(b ¼ �0:53; po0.01) and by conscientiousness (b ¼ 0:18; po0.05). By

entering these two variables, the predictive power of the remembered emo-

tional valence of past transitions disappeared: only low scores on neurot-

icism and high scores on conscientiousness were associated with high

psychological well-being (see also Perrig-Chiello & Perren, 2005).

On the one hand, our results suggest that recollection of past transitions,

especially the transition to adulthood, is significantly related to current well-

being. In fact, it has been suggested that adolescence can be seen as an

anchor point from which we begin the story of our adult lives and that the

memories from this period help us define who we are later in life (Fitzgerald,

1988; Fitzgerald & Shifley-Grove, 1999; Josselson, 1987; Rybash, 1999). On

the other , however, our results also showed that the predictive power of the

remembered emotional valence of this specific transition vanishes as soon as

personality variables are considered as predictors for current psychological

well-being. These results indicate that the reconstruction of one’s own past,

as well as the perception of one’s present state may both be driven by

personality factors.

3.2. Personality Predicts not only Biographical Memories but also Episodic

Recollection (Study 2)

As described in the two precedent studies, personality factors (especially

neuroticism) can be significant and robust predictors for biographical

recollection. Now, one could conclude that biographical reports are mere

products of motivated recollection and are consequently biased and unre-

liable. Furthermore, a memory bias of this kind might stand in contrast to

more cognitive forms of episodic recollection. The latter kind of remem-

bering might be considered to be free of influence of personality factors, and


hence, trust worthier. However, this assumption may be erroneous, and it is

reasonable to assume that personality as the individual bundle of complex

features of an integrated stable self is determining cognitive processes like

remembering in general as well. The relationship between objective memory

performance and personality factors is, at best, rather obscure. For example,

only a few investigations to date have looked at the relation between per-

sonality and episodic memory (Perrig-Chiello, Perrig, & Stahelin, 2000;

Meier, Perrig-Chiello, & Perrig, 2002), and there is a growing con-

viction that personality variables are interwoven with memory outcomes

(Cavanaugh & Green, 1990). Gold and Arbuckle (1990) proposed a model

of personality–cognition relations in old age. Recurring to the ‘‘big 5’’ cen-

tral measures (introversion–extraversion, neuroticism, openness, agreeable-

ness and conscientiousness), they hypothesized that most of these traits have

both direct and indirect effects on memory. Furthermore, they argue that, in

later life, senescent changes in the nervous system, coupled with reductions

in externally driven cognitive demands, can result in increased influences of

personality on cognitive functioning. These effects are viewed not as direct

determinants of specific skills or processes, but rather as having a more

general facilitative or detrimental effect on cognition. Neuroticism, in par-

ticular, has been associated with negative outcome expectations and with

poorer cognitive functioning (Gold & Arbuckle, 1990). Results from these

studies suggest that this approach could be a very promising way to study

interindividual differences in episodic memory.

Within the IDA-Study we had the opportunity to examine the impact of

personality on episodic memory performance in a sample of 287 healthy

adults aged 68–95 years. The purpose was to examine the contribution of

extraversion and neuroticism to the explanation of episodic memory var-

iability in old age. To assess personality, we used the two main subscales

‘‘Extraversion’’ and ‘‘Neuroticism’’ from the Freiburger Personlichkeits-

Inventar (Fahrenberg et al., 1970). For episodic memory assessment we used

a computerized test (Perrig et al., 1994). This test allows to assess different

memory dimensions (working memory, recognition and free recall). We ex-

pected higher extraversion and lower neuroticism to be related to better

memory performance. Consistent with our expectations, extraversion was

associated with higher (b ¼ 0:16; po0.01), and neuroticism was associated

with lower levels of episodic memory performance (b ¼ �0:18; po0.01) (for

more details see Meier, Perrig-Chiello, & Perrig, 2002).

In conclusion, and in line with previous research, our results indicate that

the relationship between memory and personality is not very strong, but

consistent (Arbuckle, Gold, Andres, Schwartzman, & Chaikelson, 1992;


Hultsch, Hertzog, Small, & Dixon, 1999). Even though the relationship

between personality and memory has not been studied extensively, this re-

lationship exists and deserves to be studied more fully. Overall, we conclude,

that both autobiographical and episodic memory recollection, in general,

can be predicted by personality variables. In other words, we believe that

recollection – whether episodic or semantic – are all in some way influenced

by personality factors. But this explains only a part of the variance of the

phenomenon. In order to gain a better appreciation of the determining

factors of biographical recollection, other predictors, such as actual living

context, should be taken into account. The influence of such factors is de-

scribed in the following study.

3.3. The Importance of Living Context on Biographical

Recollection (Study 3)

With respect to the results gathered so far and considering the fact that the

living context is thought to play an important role in the prediction of

biographical and episodic memory outcomes, we took the opportunity to

test this possibility using extreme group comparisons.3 We compared two

groups of middle-aged women living in two different contexts. One group

consisted of an ‘‘average’’ sample of middle-aged women (92 women from

study 2, mean age: 52 years); the other group was a subclinical sample of

women of the same age (45 women, mean age 51.56 years). The latter were

all new recruits, receiving a psychotherapeutic treatment at the time of data

collection in various outpatient clinics, for problems related to a specific

critical life course transition, namely the loss of a partner (separation/

divorce, N ¼ 35; death of spouse, N ¼ 6; combination of loss of partner and

dismissal from work, N ¼ 5). Common to all these women were symptoms

of grief due to the loss of a partner. Assessment instruments and procedure

were the same as described in Section 3.1.2.

We expected that the problem-loaded context of the subclinical group

(being in a difficult partnership transition) would lead to an overall pes-

simistic view of past biographical events. In accordance with empirical re-

sults from studies of mood-dependent information processing (having

persons with negative mood reporting more negative events (Perrig & Perrig-

Chiello, 1988; Eich & Macaulay, 2000). Together with what had been stated

in the introduction part about the function of reconstructive memories and

motivated remembering, we hypothesized that the subclinical group would

report more negative autobiographical events, and that the reported


emotional valence of their recollection would be overall more pessimistic

than that of the control group.

According to our expectations, the subclinical group did indeed report

having experienced significantly more negative transitions than the control

group (see Table 2). The emotional valence of the reported family transi-

tions, e.g. was significantly more negative in the subclinical group compared

to the controls. In particular, transitions concerning the own offspring were

rated much more negatively by the subclinical compared to the control

group (e.g. birth of the first child and departure of the last child). However,

this phenomenon and interpretation of pessimistic biographical recollection

by the subclinical group has to be restricted, due to one exception. This

exception concerns the remembered partnership translations namely, ‘‘First

love’’: Women in the subclinical group undergoing a difficult partnership-

related transition at the present time reported a significantly higher

Table 2. Reported Emotional Valence of Passed Transitions:

Comparison Subclinical Sample versus Average Group.

Emotional Valence of

Reported Transitions

Subclinical

Group

(N ¼ 45)

Average

Group

(N ¼ 92)

Z Df t p

Mean/SD Mean/SD

Number of negative

transitions

3.8/2.01 2.91/2.01 0.89 135 2.43 0.017

Emotion. valence of

transitions (total)

6.37/1.16 6.68/1.16 �0.30 134 �1.435 0.154

Emotion valence of

family transitions

6.05/1.12 6.77/2.02 �0.72 129 �2.216 0.028

Puberty 5.04/2.43 4.87/2.38 0.18 134 0.404 0.687

First job 7.18/2.81 7.8/2.42 �0.62 133 �1.333 0.185

First love 8.87/1.71 8.08/2.34 0.79 132 2.001 0.047

Departure from home 7.47/2.92 7.33/2.80 0.14 132 0.271 0.787

Marriage 7.82/2.4 7.87/2.43 �0.04 125 �0.097 0.923

Pregnancy 1 7.93/2.74 8.18/2.71 �0.25 111 �0.480 0.632

Birth 1 5.76/3.60 7.51/2.87 �1.75 111 �2.836 0.005

Separation 4.23/3.59 3.40/2.99 0.83 54 0.945 0.349

Departure of last

child

3.38/2.68 5.84/2.79 �2.47 33 �2.651 0.012

Menopause 4.63/2.94 5.64/2.03 �1.01 94 �1.917 0.058

Death of mother 2.69/2.39 3.04/1.78 �0.35 40 �0.545 0.589

Death of father 2.7/2.38 3.23/2.02 �0.54 87 �1.130 0.261


(positive) emotional valence of this romantic biographical event in the past

compared to the control group (see Table 2).

How can we explain this seemingly contradictory result? The large ma-

jority of women in the subclinical group were in the process of mourning

due to separation, divorce or death from/of their spouse. According to the

interview data, in most of the cases, the lost spouse actually was their first

love. Furthermore, this first love was remembered as being highly positive,

and compared to other people’s first love, ‘‘much better’’ (social compar-

ison: 6.42 versus 5.32, p o0.01). Could this be a compensating mechanism –

a self-serving biased recollection – aimed at reducing self-doubts concerning

this new broken long-term relation? Or, for the women to give a plausible

socially accepted explanation for their excessively strong, psychological

reaction to that loss?

In conclusion, we can say that there is a difference between knowing and

remembering our past. The current living context not only determines per-

ception of the actual situation but systematically bias reports on earlier life

events – for good or bad. Remembering our past goes beyond reporting

autobiographical facts, and implies reconstructive recollection and interpre-

tation, motivated by the perception, that is the interpretation or meaning of

the actual situation. Again, we would emphasize that these processes need

not to be deliberate or accompanied by conscious insight. Especially in

periods of change, growth, when new challenges arise, biographical memory

seems to be tuned to operate selectively (Fitzgerald & Shifley-Grove, 1999).

Distortions and self-serving biases are inevitably associated with this

recollective process.

4. DISCUSSION

The aim of this contribution was to demonstrate possible psychological

sources of biased remembering of autobiographical events and their relation

to well-being based on data from two research programs on middle and old

age. Our results suggest that the recollection of passed life events is asso-

ciated with personality variables (especially neuroticism and extraversion).

Current well-being is related to the emotional valence of biographical rec-

ollection. This latter relationship, however, seems to be mediated by the

personality variables, because it disappears as soon as personality variables

are introduced into the regression analysis, at least for episodic memory

performance, in general. Our finding fits well with the theoretical framework

of constructive processes in perception and memory (Bartlett, 1932/1993;


Schacter, 1996). Personality ‘‘schemata’’ and personality ‘‘attitudes’’, or, in

other words, the organized set of autobiographical knowledge that consti-

tutes our self-identity, relates past and present well-being in a consequent

meaningful way.

Finally, from a developmental perspective, several authors have also un-

derlined the importance of subjective reconstruction of life for current and

future well-being. For example, Erikson (1959) had stated that the ability to

really see one’s self requires a continuous perspective, both in retrospect and

prospect. It mandates linking the presently understood past and the antic-

ipated future with the experiences present in the individual. In a similar vein,

for McAdams (1993) life stories bind together events in time, organizing

present reality by connecting past and future. These stories are less about

facts and more about meaning. In the subjective telling of the past, the past

is constructed. Butler (1963) has postulated the adaptive function of life

review: Achievement of integrity in such a life review promotes successful

aging. Life review has the function of equilibrating the sense of self-worth,

coherence and of reconciliation of one’s past and present. In this sense, the

remembered emotional valence of past transitions should not (and probably

cannot) be measured exclusively in terms of true/not true, but rather in

terms of preserving a sense of coherence, of continuity of the ‘‘self’’. Ryff

(1991) has suggested that comparisons of the self in the present with the self

in the past constitute an important source for equilibrating actual well-being

for adults (see also Suls & Mullen, 1982). And finally, Keyes and Ryff (1999)

have pointed out that this is how people construe their experiences, not just

experience per se, that matters. The meaning people attach to some events

reflects a concern for maintaining a favorable image.

Our results also provide additional empirical evidence that not only auto-

biographical recollection is influenced by personality, but episodic memory

in general. From our findings presented here, one might assume that per-

sonality predicts better episodic recollection in memory tests, while the ac-

tual living context is more predictive in autobiographical reports. Hence, in

order to predict subjective biographical recollection more accurately, actual

well-being and living context have to be taken into account. In fact, our

results on extreme group comparison show that retrospective recollection and

evaluation of past transitions reflect not only conscious phenomenological re-

experiencing of real-life events, but also a person’s motivation of recon-

structing his/her life in order to gain a sense of coherence and control. In this

way, autobiographical reports can be considered to be the result – to a sub-

stantial degree – of an internal dynamics of the psyche, and as such, they may

not be available to conscious introspection. According to Schacter (1996), the


complex mixture of personal knowledge that we retain about our past is

woven together to form our life stories. These are the biographies of self that

provide narrative continuity between past and future – a set of memories that

form the core of personal identity. On how exactly personality does influence

the emergence of actual living conditions, or on how exactly personality

relates to cognitive processes – or vice versa – must remain open here, but

should be a most important research topic for the future. To unwrap this

complex package of hidden causalities provides a formidable challenge for

psychological investigations and promises large steps forward in understand-

ing better the human being.

NOTES

1. National Research Program 32 ‘‘Ageing’’, Swiss National Science Foundation.2. Swiss Priority Program ‘‘Switzerland toward the Future’’.3. Original data based on a master thesis (Rusca, 2003), performed within the

research program ‘‘Transitions and Life Perspectives in Middle Age’’ under super-vision of the first author.

ACKNOWLEDGMENTS

This research was supported by grants by the Swiss National Science Foun-

dation (No. 4032-035642 and No. 5004-058457). We wish to express our

gratefulness to Dr. Josephine Cock, University of Berne, for her helpful

comments on an earlier draft.

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APPENDIX. LIFE EVENT INVENTORY

Basle Interdisciplinary Study on Aging (IDA-Study)

Life Event Inventory

Which important events, positive as well as negative, come to mind if you

think back the last ten years?

I will provide some topics/themes. Please tell me in each case which event(s)

come to mind and how you experience those events at present.


positive negative

Employment, work .....................(i.e. retirement or retirement of one's ... spouse/partner, change of job)..............

2

22

1

11

0

00

ambivalent

Illness and Health ................................

(i.e. injury, illness concerning oneself,

or one's spouse/partner, children).........

2

2

2

1

1

1

0

0

0

Residence ..............................................

(move, relocation i.e. move to another .

town, renovation) ...................................

2

2

2

1

1

1

0

0

0

Death ......................................................

(spouse/partner, children, other family .

members) ................................................

2

2

2

1

1

1

0

0

0

Partnership and Familial situation ...

(separation, divorce, ..............................

grandparenthood) ...................................

2

2

2

1

1

1

0

0

0

..................................................................

..................................................................

Financial situation ...............................2

2

2

1

1

1

0

0

0

Recreation, entertainment ..................

(travel, social activities).........................

2

2

2

1

1

1

0

0

0



236

STUDYING LIVES IN TIME:

A NARRATIVE APPROACH

Dan P. McAdams

Social scientists conceive of the human life course in many different ways.

Life-span developmental theorists like Erikson (1963) and Levinson (1978)

imagine a sequence of stages through which the individual passes on the

journey of life. Each period or season offers its own distinctive challenges,

and each individual confronts those challenges in a predetermined and pre-

dictable order. More sociologically informed theorists invoke social roles,

generational cohorts, and historical events in their conceptions of human

lives in time, suggesting that development is rather too contingent and con-

textual to conform to any predetermined sequence (Dannefer, 1984; Elder,

1995). The person moves through time along a dynamic trajectory that

results from the complex interplay between human agency and the manifold

forces of social structure. A third approach in the social sciences argues for

the primacy of personal traits in shaping the life course (McCrae & Costa,

1990; Roberts & Pomerantz, 2004). Human beings differ with respect to

broad psychological tendencies that are surprisingly consistent, stable, and

pervasive over developmental time. While contexts may change, personal

dispositions continue to exert their steady and predictable influence.

Conceptions of the life course that emphasize developmental stages, con-

tingent trajectories, and personal traits all have considerable value in mak-

ing sense of how individual lives evolve over time. Stage theories spell out

common developmental demands that people face in many different cultures





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10009-4

237

and contexts. Theories emphasizing context and contingency in the life

course pay careful attention to economic, social, cultural, and historical

forces that impact lives in variable ways, and how individuals react to and

sometimes resist those forces. Trait conceptions underscore the importance

of human individuality, and how people construct lives that are consistent

with their internal dispositions. For all their value, however, these three

major approaches to the life course pay only passing attention to the sub-

jective meanings of lives. How do individuals themselves make sense of their

lives in time? What meanings do persons construct for themselves as they

move through developmental stages, confront the vagaries of off-time and

on-time challenges in the life course, and act in accord with their internal

dispositions?

The current chapter affirms a fourth approach to the life course, one that

emphasizes the meanings that people make of their own lives in time. The

chapter’s thesis is that those meanings often take the form of stories (Cohler,

1982; McAdams, 1985; Polkinghorne, 1988). People construe their own lives

as evolving stories that aim to reconstruct the past and imagine the future in

meaningful and coherent ways. Narrative approaches to the life course see

persons as storytellers, see lives as stories told, and see the life course as a

psychosocial construction reflecting both personal inclinations and the nar-

rative conventions and traditions that prevail in a given society.

PERSONALITY, NARRATIVE, AND

THE LIFE COURSE

The last two decades have witnessed an upsurge of interest among scholars

and social scientists in the role of stories in social life. Narrative approaches

have had a major impact in sociology (Holstein & Gubrium, 2000), crim-

inology (Maruna, 2001), gerontology (Birren, Kenyon, Ruth, Shroots, &

Svendson, 1996), organizational studies (Gabriel, 2000), educational re-

search (Casey, 1996; Ely, 2003), theology and moral philosophy (MacIntyre,

1984), developmental psychology (Fivush & Haden, 2003), social psycho-

logy (Gergen, 1991; Murray & Holmes, 1994), clinical psychology (Angus &

McLeod, 2004; Lieblich, McAdams, & Josselson, 2004), and the cognitive

sciences (Conway & Pleydell-Pearce, 2000; Schank & Abelson, 1995).

Bruner (1990) and Sarbin (1986) deem narrative to be a new and powerful

root metaphor for the social sciences. Josselson and Lieblich (1993) describe

the narrative study of lives as a loosely organized, interdisciplinary endeavor

DAN P. McADAMS238

aimed at telling and analyzing the stories people construct to make sense of

social life in time, with some special emphasis given over to narrative ac-

counts, such as those told by women and people of color, that have his-

torically been suppressed or marginalized (see also Franz & Stewart, 1994;

Rosenwald & Ochberg, 1992). The many different approaches today in

social science incorporating narrative range from those employing story-

telling methodologies to examine social behavior (e.g., Baumeister, 1994;

Woike, 1995) to those promoting narrative theories about social life itself,

viewing lives as ongoing stories that are told, revised, and retold in culture

(e.g., Gregg, 1991; Hermans, 1996).

The particular narrative approach to be described in the current chapter

evolved within the field of personality psychology (McAdams, 1985), though

its origins were strongly influenced by the approaches in life-span develop-

mental psychology (Erikson, 1963), life course sociology (Bertaux, 1981),

Sartrean philosophy (Charme, 1984), and studies of narrative and bio-

graphy in the humanities (Edel, 1978; Langbaum, 1982; Ricoeur, 1984).

Personality psychology views itself as the scientific study of the whole per-

son (Allport, 1937; McAdams, 1997). The aim of personality psychology is

to provide a scientifically credible account of human individuality – how the

individual person is (a) like all other persons, (b) like some other person, and

(c) like no other person (Kluckhohn & Murray, 1953). As such, personality

psychology considers both species-typical characteristics of human nature

and individual differences between people. Research in the field, however,

tends to focus mainly on individual differences. What are the most impor-

tant individual differences in psychological and social life? Current thinking

in personality psychology tends to focus on three different classes or levels

of individual differences – dispositional traits, characteristic adaptations,

and integrative life stories (Hooker, 2002; Hooker & McAdams, 2003;

McAdams, 1995, 2006a; Sheldon, 2004). From the standpoint of personality

psychology, then, life narrative holds a particularly prominent position

within an evolving constellation of characteristics – including traits and

adaptations – that specify how a person is similar to and different from

other persons.

Within the constellation of human individuality, the most recognizable

and longitudinally consistent aspect of the person is his or her dispositional

traits (Matthews, Deary, & Whiteman, 2003; McCrae & Costa, 1990).

Dispositional traits are those broad, stylistic, and linear dimensions of hu-

man individuality, bearing names such as ‘‘extraversion/introversion’’ and

‘‘depressiveness,’’ that account for general consistencies in behavior,

thought, and feeling across situations and over time. While traits were once

Studying Lives in Time: A Narrative Approach 239

the object of considerable derision among those psychologists who empha-

size the role of situations in shaping behavior (e.g., Mischel, 1968), traits

today are the coin of the realm in personality research. The scientific viability

of the trait concept comes from many sources. First, a consistent body of

research shows that trait attributions based on careful observations reflect

real differences in the behavior and personalities of the people about whom

the attributions are being made (Funder & Colvin, 1991). Second, research

consistently shows that scores on self-report trait scales predict general con-

sistencies in behavior when sampled across different situations and over time

(Kenrick & Funder, 1988) and predict important outcomes such as well-

being, health, and even longevity (Friedman et al., 1993; Matthews et al.,

2003). Third, individual differences in self-report trait scores show consid-

erable stability over developmental time, especially during the adult years

(Costa & McCrae, 1994; Roberts & Del Vecchio, 2000). Fourth, twin studies

suggest that traits are at least moderately heritable, with as much as 50–60%

of the variance in trait scores being attributed to genetic differences between

people (Bouchard, Lykken, McGue, Segal, & Tellegen, 1990). Fifth, 30 years

of factor-analytic studies have resulted in an emerging consensus in the field

of personality regarding the organization of the trait universe. Many per-

sonality psychologists today subscribe to the Big Five model of traits,

grouping the many possible traits that might be invoked in describing human

individuality into the five general categories of extraversion/introversion,

neuroticism, conscientiousness, agreeableness, and openness to experience

(Goldberg, 1993; John & Srivastava, 1999; McCrae & Costa, 1990).

Showing considerable stability over time, the Big Five traits provide

something akin to a dispositional signature for human lives. A highly

extraverted person tends to express high levels of energy and social interest,

enjoys being with people, and seeks opportunities for social stimulation to a

greater extent, on average, than does a highly introverted (low extraversion)

person. If a person scores high on an extraversion scale at, say, age 30, he or

she is likely to score fairly high again 20 years down the road, at age 50. If

the person is low on extraversion at Time 1, chances are good that he or she

will score low again at Time 2. In other words, people tend to hold their

relative position in the normal distribution of scores for any given trait. Of

course, this differential continuity in traits is not perfect. People can and do

move around in the distribution over time (Roberts & Pomerantz, 2004).

But the general trend is toward relative stability. Traits are well designed to

show the interindividual stability of personality over the long haul. But

this stability is of a very general kind, referring to a signature style of

behavior, thought, and feeling. The style may be apparent very early on in

DAN P. McADAMS240

an interpersonal interaction, even within the first minute or two (Ambady,

Hallahan, & Rosenthal, 1995). But as one gets to know a person better, one

begins to see aspects of human individuality that appear to go beyond traits,

aspects that are also subject to considerable change over the life course

(McAdams, 1994). Personality begins with traits, but it does not end there.

At a second level of human individuality, personality psychologists speak

of personal motives, goals, values, interests, strategies, schemas, stage-

specific concerns, and conditional patterns of behavior that are situated in

time, place, and/or social roles (Cantor, 1990; Emmons, 1986; Freund &

Baltes, 2000; Little, 1999; Mischel & Shoda, 1995; Schwartz, 1994; Winter,

John, Stewart, Klohnen, & Duncan, 1998). These many different aspects of

human individuality may be provisionally grouped under the label charac-

teristic adaptations. If dispositional traits sketch an outline of human in-

dividuality, characteristic adaptations fill in some of the details. Though

sometimes loosely related to dispositional traits, people’s goals, plans, sche-

mas, coping strategies, and the like express the highly specific and person-

alized ways in which they have adapted to the variegated demands of social

life. Unlike traits, many characteristic adaptations are expected to change

substantially over the life course, as the circumstances of life change and as

new developmental issues arise. For example, longitudinal and cross-

sectional studies show that adults’ concern for the next generation – what

Erikson (1963) called the developmental task of generativity – rise and fall

over the adult life course, reaching something of a peak in the middle-adult

years (McAdams, 2001; McAdams, de St. Aubin, & Logan, 1993; Rossi,

2001; Vaillant & Milofsky, 1980).

But traits and adaptations do not provide the full picture. People differ

from each other in ways that go beyond their dispositional signature and

their characteristic motivational and developmental concerns, and this

becomes especially apparent as people move into and through their adult

years. At a third level of human individuality, then, reside integrative life

narratives (Hooker & McAdams, 2003; McAdams, 1995, 1996). These are

the internalized and evolving stories of the self that people construct to

make sense of their lives in time. These stories, or narrative identities (Singer,

2004), combine a person’s selective reconstruction of the past with his or her

imagined anticipation of the personal future to produce a story with plot

and characters to explain who I am, how I came to be, and where my life is

going in the future. As Giddens (1991) writes, ‘‘a person’s identity is not

to be found in behavior, nor – important though this is – in the reactions

of others, but in the capacity to keep a particular narrative going’’ (p. 54).

Narrative identities provide life with some degree of unity and purpose.


As people develop over the life course, they rewrite their own self-defining

life stories, albeit in typically implicit and unconscious ways. If, then, dis-

positional traits sketch the outline and characteristic adaptations fill in some

of the details of human individuality, integrative life stories speak to what a

person’s life means in the overall, set in subjective time. Personality itself,

then, is a unique patterning of traits, adaptations, and stories evolving in a

complex interpersonal, social, cultural, and historical context.

THE DEVELOPMENT OF THE STORY

Stories are fundamentally about the vicissitudes of human intention organ-

ized in time (Bruner, 1990; Ricoeur, 1984). The developmental origins of

storytelling, therefore, may be traced back to infancy. By the end of the first

year of life, human infants appear to have a rudimentary sense of human

intentionality, as indexed by their preference for observing and imitating

goal-directed rather than random behaviors (Tomasello, 2000). By age 2,

children have developed what Howe and Courage (1997) call an auto-

biographical self. They begin to construe the world from the standpoint of a

subjective, appropriating self (what William James called ‘‘the I’’) who nar-

rates and remembers personal events as episodes that happen to ‘‘me.’’

Parents typically assist children in the encoding, recollection, and telling of

personal memories, encouraging their effort to narrate their experiences and

acquainting them with the conventions for good storytelling (Fivush &

Haden, 2003). By the time the children have reached their fourth birthday,

they typically understand that stories contain characters who seek to enact

their intentions, characters who want things and try to get them over time.

With few exceptions, young children come to understand that people have

minds, that minds contain desires and thoughts, and that people act upon

those desires and thoughts in goal-directed ways (Wellman, 1993). By age 5,

they are able to tell stories about the self that express this implicit under-

standing of human minds and human intentionality. By the age of 5, they

have developed a story schema, or a set of expectations regarding what a

story should look and sound like (Mandler, 1984). A story should be set in a

particular time and place. It should involve a motivated character who sets

out to get or do something. The character should eventually run into ob-

stacles in the goal-directed action, which will lead to reactions and changes

and ultimately some kind of a resolution. When stories do not conform to

the schema, children find them odd, and they may seek to reformulate

noncanonical narratives so that they fit the schema better.

DAN P. McADAMS242

Although children can tell stories about their own experiences by age 5,

they do not have, nor are they really working on, narrative identities. Chil-

dren do not have the developmental maturity, the social experiences, nor the

cognitive skills to see their own lives as stories evolving over time, stories for

which they are both the author and the protagonist. Beginning with Erikson

(1963), many developmental theorists have argued that people do not con-

struct full-fledged identities until adolescence or young adulthood (Arnett,

2000). McAdams (1985) suggests that what Erikson viewed as the challenge

of identity formation in adolescence and young adulthood is largely about

formulating a story for one’s life – what Sartre called a true novel (Charme,

1984) – that selectively reconstructs the past and imagines the future as an

integrated temporal whole, to provide life with meaning and purpose and

situate the person’s imagined life trajectory within a recognizable societal

niche. As Hankiss (1981) suggested, the challenge of identity involves

‘‘mythologically re-arranging’’ one’s life history into a coherent narrative of

self (p. 203). Similarly, Kohli (1981) suggested that the creation of a life

story presupposes an advanced facility with autobiographical forms: ‘‘The

autobiographical form presupposes a developed individuality, a self-

conscious ‘I’ being able to grasp itself as the organizer of its own life

history’’ (p. 64). A number of theorists contend that the full utilization of

the autobiographical form typically awaits late adolescence.

For example, Habermas and Bluck (2000) argue that the ability to con-

strue one’s life as an evolving narrative of the self that integrates the re-

constructed past and imagined future requires mastery of four different

cognitive skills. First, the individual must be able to order events into tem-

poral sequences, what Habermas and Bluck (2000) refer to as temporal

coherence. Second, the individual must be able to assimilate his or her life to

what society deems to be the typical course of life, showing what Habermas

and Bluck (2000) call autobiographical coherence. Third, Habermas and

Bluck identify causal coherence as the ability to link different auto-

biographical events into causal sequences to explain how a particular aspect

of the self came to be. A teenager may explain her distrust of authority by

telling how she used to trust her father completely, but how she gradually

lost confidence in him through a series of events wherein he disappointed

her. Finally, life storytelling requires what Habermas and Bluck call the-

matic coherence – the inductive derivation of themes or principles from a

series of personal events. A man may come to conclude that ‘‘I am some-

body who follows my deepest dreams’’ by noting how in many different

events in the past he defied convention and acted in accord with his most

cherished beliefs. Habermas and Bluck (2000) review research, showing that


facility with causal and thematic coherence tends not to appear in self-

narration until the late teenage years.

Equipped with a full complement of cognitive skills necessary for life

narration and encouraged by their psychosocial environments to figure out

who they are and how they are to fit into the adult world, older adolescents

and young adults – what Arnett (2000) terms emerging adults – typically seek

to arrange their lives into narrative identities. The move toward narrative

identity in emerging adulthood is especially common in modern societies,

wherein emerging adults are strongly urged to build a life that both ex-

presses their individualized tastes and proclivities and conforms in some

manner to the exigencies of a rapidly changing economic and cultural world

(Gergen, 1991; Giddens, 1991; McAdams, 1996). The development of nar-

rative identity in the emerging adult years occurs gradually, unevenly, and in

the context of close personal relationships (McAdams, 1993; Thorne, 2000).

Emerging adults try out different stories for their lives, imagine and tell past

events and future goals in different ways, with different friends and family

members, for different purposes and effects, in a social context wherein

other emerging adults are doing pretty much the same thing. Stories are

shared and refined. Stories are revised to reflect changing priorities, new

insights, transformed goals and beliefs. Eventually, many people settle on a

few key story lines, accentuate a set of key events from the past and down-

play many others, imagine a circumscribed set of alternative and yet more-

or-less realistic scenarios for the future, and gradually give their lives a

narrative shape.

Emerging adulthood is prime time for the development of narrative iden-

tity. But the process of constructing one’s life as a narrative does not end in

one’s 20s. People continue to work on their life stories across the bulk of the

adult life span (Birren et al., 1996; Cohler, 1982; Hooker & McAdams, 2003;

McAdams, 1993). The midlife years, for instance, may be occasioned by

considerable identity work for many modern adults. Life-stage theorists

have written about how the realization that one’s life is now more than half

over can bring to the psychosocial fore concerns about loss and mortality

and can stimulate the actualization of long-suppressed tendencies, such as

traditionally masculine tendencies in women and feminine tendencies among

men (Gutmann, 1987; Levinson, 1978). Life course theories emphasize

changing social roles and relationships in the midlife years and shifting

contingencies in the social ecology of everyday life (Elder, 1995). Theorists

of many different stripes tend to agree that midlife can be a period wherein

considerable revision of one’s life story is likely to occur. Even as one’s traits

continue to express continuity in selfhood, people change their narrative

DAN P. McADAMS244

understandings of the past, present, and future in response to a wide range

of expected and unexpected life events and transitions, from off-time

setbacks like the premature death of a spouse to on-time markers like the

children leaving home to go to college, one’s 40th birthday, or the transition

from work to retirement.

Changes in narrative identity constitute real personality change – just as

real as changes in dispositional traits, but different. Changes in narrative

identity are typically the kinds of changes that people allude to when they

maintain that they have ‘‘matured’’ with ‘‘experience,’’ that they are ‘‘very

different people’’ today than they once were (McAdams, 1994). They are the

kinds of changes, furthermore, that therapists and counselors often try to

effect in their clients (Angus & McLeod, 2004). Conceptions of personality

that focus exclusively on traits, therefore, underestimate the nature and

extent of development over the span of life. Stories describe change and

development in life, and life stories themselves change and develop over the

life course.

STUDYING LIFE STORIES

Social scientists have developed many different methodologies for studying

narrative identity. Some researchers prefer in-depth interviews while others

collect short written accounts of important life scenes. Some researchers

engage in a deep, hermeneutical process for each case while others employ

content-analysis systems to many cases in an effort to quantify results.

Within personality psychology, narrative researchers have gathered in-depth

case data from interviews and short written accounts through open-ended

questionnaires (McAdams, 1999; Singer, 2004). Most personality research-

ers, furthermore, have tried to quantify their data in order to test hypotheses

about life stories in select samples of individuals. For example, research has

examined the relations between aspects or features of life stories on the one

hand and traits (McAdams et al., 2004), social motives (Woike, 1995), val-

ues and personal ideologies (de St. Aubin, 1996), coping with stress (King,

Scollon, Ramsey, & Williams, 2000), mental health and well-being (Bauer &

McAdams, 2004), drug and alcohol abuse (Singer, 1997), and patterns of

interpersonal relationships (Thorne, 2000) on the other.

One standard procedure used in a number of studies is McAdams’s (1993)

Life Story Interview. The interviewer begins by asking the participant to

think about his or her life as if it were a book and to provide a brief outline

of that book. Then the interviewer zeroes in on particular scenes or mo-

ments that stand out in the story – high points, low points, and turning


points, for example. For each of these scenes, the participant describes in

detail what happened in the event, who was there, what he or she was

thinking and feeling in the scene, and what he or she thinks the scene may

say about who the person is, was, or will be. Many of these key scenes

qualify for what Singer (1995) has termed self-defining memories – vivid,

affectively charged events from the past that express key hopes, fears, and

conflicts in life. The interviewer then moves to the future and asks the

participant to imagine what the next chapter in his or her life story might be

like, focusing on dreams and fears regarding the future. Toward the end of

the interview, the participant considers more abstract and philosophical

questions regarding the life story, articulating those religious and/or social

beliefs and values that may be important in the narrative and commenting

on what message or moral the story seems to express.

Depending on the purpose of the investigation, accounts obtained from

the Life Story Interview, and similar procedures, may be analyzed according

to many different systems, some of which emphasize structural features such

as narrative coherence and complexity and others of which emphasize story

content. With respect to content, a number of researchers have examined

Bakan’s (1966) distinction between agency and communion in life-narrative

accounts (e.g., Hermans, 1996; McAdams, 1985; McAdams, Hoffman,

Mansfield, & Day, 1996; Woike, 1995). Agency refers to characters’ strivings

to expand, assert, control, or defend the self, and is manifested in themes

related to achievement, power, status, and self-mastery. By contrast, com-

munion refers to characters’ strivings to connect the self with others, man-

ifested through themes of friendship and love, interpersonal

communication, caring for and helping others, and feeling a sense of com-

munity. In their various manifestations, agency and communion appear to

be highly salient themes in life stories and common dimensions upon which

stories can be said to differ in meaningful ways.

Another thematic distinction that has enjoyed currency in recent research

is that between contamination sequences and redemption sequences in life

narrative (McAdams & Bowman, 2001; McAdams, Reynolds, Lewis,

Patten, & Bowman, 2001). In a contamination sequence, an affectively

positive scene is rather suddenly transformed into something negative. The

main character’s initial state of strong joy or excitement suddenly gives way

to fear, sadness, shame, guilt, or some other negative emotional reaction.

The scene’s opening goodness is contaminated, ruined by what follows.

Most everybody can recall contamination sequence from their lives. But

some individuals use this narrative form in making sense of their lives more

often than do others. Research suggests that adults who express more

DAN P. McADAMS246

examples of contamination in their life narrative accounts tend to score

much lower on measures of psychological well-being and higher on meas-

ures of depression compared to individuals who show fewer contamination

sequences in life stories (McAdams et al., 2001). In a study of 74 midlife

adults, self-report measures of self-esteem, life satisfaction, and sense of life

coherence were negatively correlated with, and depression positively corre-

lated with, scores on contamination sequences derived from content analysis

of the life narrative interviews. Contamination sequence scores were also

significantly associated with a narrative measure of depressogenic attribut-

ional style – the tendency to attribute negative life events to internal, stable,

and global causes (Peterson & Seligman, 1984). But contamination sequence

scores from these same life stories proved to be a significantly strong pre-

dictor of depression than was depressogenic attributional style (Adler,

Kissel, & McAdams, in press).

In a redemption sequence, a bad or affectively negative life-story scene

turns good – the bad is salvaged or redeemed by the positive outcome that

ensues (McAdams et al., 2001). Perhaps not surprisingly, the redemptive

form of life storytelling tends to be positively associated with a number of

positive psychosocial features – such as life satisfaction among midlife

American adults and among college students (McAdams et al., 2001). In one

study, researchers coded written accounts of 10 key scenes in life narratives

from 125 college students. Redemption imagery in the stories was positively

associated with numerous self-report measures of well-being, such as life

satisfaction and purpose in life. The researchers also coded the overall

emotional quality of the stories, from positive to negative. The results

showed that redemptive stories are not exactly the same thing as happy,

positive stories about the self. Redemption sequences were only weakly

correlated with overall emotional positivity of the life stories. Furthermore,

redemption sequences proved to be substantially stronger predictors of well-

being than did the overall emotional tone of the narrative accounts. The

results suggest that it is not so much that happy people tell happy stories

about their lives. Instead, happy people tend to tell life stories filled with

episodes in which suffering is redeemed by positive outcomes.

Redemption sequences are at the center of a collection of narrative themes

that tend to characterize the life stories told by highly generative American

adults. In a series of studies, McAdams and colleagues (Mansfield & McAdams,

1996; McAdams & Bowman, 2001; McAdams, Diamond, de St. Aubin, &

Mansfield, 1997; McAdams, Ruetzel, & Foley, 1986; McAdams et al., 2001;

Van de Water & McAdams, 1989) collected life-narrative accounts from

midlife adults who score especially high on objective indices of generativity.


Generativity is an adult’s concern for and commitment to promoting the

well-being of future generations. A growing body of research shows that

adults who score high on generativity measures tend to be more deeply

invested in a wide range of endeavors aimed at improving the world around

them – from parenting to volunteer work to voting (McAdams, 2001;

McAdams & de St. Aubin, 1992; Rossi, 2001). Compared to their coun-

terparts scoring lower in generativity, moreover, highly generative adults

tend to construct life stories that feature a wide range of variations on the

theme of redemption. In addition, their stories often feature these related

themes: (1) a childhood sense of feeling special or advantaged; (2) an early

sensitivity to the suffering or oppression of others; (3) the consolidation of a

simple but compelling personal ideology in adolescence and the commit-

ment to that ideology through the adult years; (4) tension between agentic

and communal strivings in adulthood; and (5) anticipating growth and fru-

ition for the future. Taken together, these themes comprise what McAdams

(2006b) calls the redemptive self. Table 1 summarizes the central themes of

the redemptive self.

The redemptive self represents a particular kind of narrative identity that

serves well the psychosocial needs of many highly generative men and

women in their midlife years. The story reinforces their commitments to

promoting the well-being of future generations. Believing that they were

once the beneficiaries of early advantages in life, they feel some obligation to

give back for the blessings they received. Their stories tell them that early on

they were chosen or advantaged in some way in a world where many other

people suffer. The contrast between their sense of personal blessing and their

early awareness of the suffering of others may set up in their story a moral

challenge and a felt obligation to be of some good use to others (Colby &

Damon, 1992). The beliefs they consolidated in adolescence serve as their

steady guides. In that generativity is often hard work, they manage to per-

severe in part because their story tells them that bad things are often over-

come, suffering gives way to redemption. They look to the future with hope,

even as they realize that the world is a very dangerous place. The hero in the

story is the dogged, if sometimes guileless, protagonist who remains upbeat

and focused on making a positive difference in his or her family, neighbor-

hood, church, community, society – even against the odds.

McAdams (2006b) also suggests that the redemptive self, for better and

for worse, may represent a characteristically American way of narrating a

caring and productive life at midlife. As highly generative American adults

shape their lives into redemptive narratives, they implicitly draw upon an

optimistic and highly individualistic understanding of the life course

DAN P. McADAMS248

that celebrates personal redemption through the discourses of atonement

(religion), emancipation (politics), recovery (medicine), self-actualization

(psychology), and upward social mobility (economics). These discourses

have their origins in such canonical American texts as the spiritual

autobiographies of the New England Puritans, Benjamin Franklin’s auto-

biography, Emerson’s 19th-century lectures and essays on ‘‘self-reliance,’’

Horatio Alger stories, the Gettysburg Address, and the powerful narratives

written by escaped slaves before the American Civil War. Variations on

the same themes run through 20th-century American autobiography and

fiction, American television and movies, and the burgeoning literature

of American self-help. Translated into myths of national identity, these

themes are reflected in such quintessentially American notions as ‘‘the cho-

Table 1. The Redemptive Self: Six Themes Characterizing Life Stories

Constructed by Highly Generative American Adults.

1. Early advantage As a young child, the story’s protagonist enjoys a special

advantage or blessing that singles him or her out in the family

or vis-a-vis peers. From an early age onward, the protagonist

feels that he or she is special in a positive way.

2. Suffering of others Early in the story, the protagonist witnesses the suffering or

misfortune of other people and feels sympathy or empathy

for them. Objects of the protagonist’s concern might include

the sick, dying, disabled, mentally ill, economically

disadvantaged, or any of a number of other groups or

individuals that might require special care or help.

3. Ideological

steadfastness

By adolescence, the protagonist has established a clear and

coherent belief system that governs his or her life. The belief

system, often rooted in religion, remains relatively stable and

steadfast over time. Once the belief system is established, the

protagonist does not experience profound ideological doubt,

uncertainty, or crisis.

4. Redemption

sequences

Bad or affectively negative life events are immediately followed

by good or affectively positive outcomes. The bad scene is

redeemed, salvaged, and made better by what follows.

5. Power vs. love As an adult, the protagonist repeatedly finds that strong agentic

desires to distinguish the self by having a positive impact on

the world repeatedly conflict with equally strong communal

desires to form loving relationships and be accepted by others

as an equal.

6. Prosocial future In looking to the future chapters of the life story, the

protagonist sets goals that aim to benefit society in general or

its institutions.

Source: McAdams (2006b) and McAdams et al. (1997).


sen people,’’ ‘‘manifest destiny,’’ and ‘‘the American dream.’’ The life stories

constructed by highly generative American adults today employ rich met-

aphors and ways of thinking about identity that Americans have both

cherished and contested, found both inspiring and problematic, for over 300

years – from the Puritan landing in New England in 1630 to the most recent

episodes of The Oprah Winfrey Show.

NARRATIVE, CULTURE, AND THE LIFE COURSE

Research on the redemptive self suggests that life stories may say as much

about the culture and society wherein the narrator lives as they do about the

narrator’s life itself. Many social scientists see life stories as cultural texts

(Rosenwald & Ochberg, 1992; Shotter & Gergen, 1989). Life stories mirror

the culture wherein the story is made and told. Stories live in culture. They

are born, they grow, they proliferate, and they eventually die according to the

norms, rules, and traditions that prevail in a given society, according to the

society’s implicit understandings of what counts as a tellable story, a tellable

life. As Rosenwald (1992) puts it, ‘‘when people tell life stories, they do so in

accordance with the models of intelligibility specific to the culture’’ (p. 265).

Habermas and Bluck (2000) contend that before a person can formulate a

convincing life story, he or she must become acquainted with the culture’s

concept of biography. In modern Western cultures, Denzin (1989) and

McAdams (1996) suggest, biographies are expected to begin in the family, to

involve growth and expansion in the early years, to trace later problems back

to earlier conflicts, to incorporate epiphanies and turning points that mark

change in the protagonist’s quest, and to be couched in the discourse of

progress versus decline. But other societies tell about lives in different ways

and have different views of what constitutes a good story to tell (Gregg, 1991).

Even within a given society, furthermore, different stories compete for

dominance and acceptance. Feminists such as Heilbrun (1988) argue that in

Western societies many women ‘‘have been deprived of the narratives, or the

texts, plots, or examples, by which they might assume power over – take

control over – their lives’’ (p. 17). It is painfully clear that life stories echo

gender and class constructions in society and reflect, in one way or another,

prevailing patterns of hegemony in the economic, political, and cultural

contexts wherein human lives are situated. Power elites in society privilege

certain life stories over others, and therefore a number of narrative re-

searchers and clinicians seek to give voice and expression to forms of life

DAN P. McADAMS250

narrative that have traditionally been suppressed or marginalized (Franz &

Stewart, 1994; White & Epston, 1990).

At the same time, it is clear that people, even those whose lives do not

affirm a society’s dominant narratives, do not simply acquiesce to prevailing

cultural norms and standards with respect to the narrative construction of

lives. Human agency confronts the instrumental and expressive givens in a

particular social ecology and seeks to appropriate what it can, picking and

choosing among different narrative options, making a life and a life story

that may even defy the status quo. Gjerde (2004) argues that the relation

between self and culture is complex and often contested. People sometimes

resist the norms to construct individual life patterns that defy cultural con-

vention. Gjerde (2004) writes: ‘‘Culture can be said to exist as contested

representations situated in public domains or institutions in which power is

both exercised and resisted’’ (p. 146). Consequently, life stories represent

something of a resultant compromise between human agency and social

structure. Narrative identity is a psychosocial construction – a joint product

of individual and society.

Life stories draw on the stories that people learn as active participants in

culture – stories about childhood, adolescence, adulthood, and aging.

Stories capture and elaborate metaphors and images that are especially

resonant in a given culture. Stories distinguish between what culture glorifies

as good characters and vilifies as bad characters, and they present the many

varieties who fall in between. Stories depict full and fragmented lives that

are exciting, frightening, infuriating, enlightening, admirable, heroic, digni-

fied, ignoble, disgusting, wise, foolish, and boring. Stories teach people how

to live and what their lives may mean. Stories also spell out expected stages

and trajectories in the life course. From a narrative perspective, human lives

do not follow a natural stage-like progression; they do not conform readily

to social roles and norms; and they do not follow the predictable trends

ascribed to a given dispositional trait. Instead, life stories follow whatever

form the individual and society manage to work out. In the best cases, that

form meets what the individual feels to be his or her inner needs and

aspirations while making a productive contribution within a specified psy-

chosocial niche. In cases that are less ideal but probably more common,

individuals struggle to find life-narrative forms that more-or-less see them

through a difficult life terrain, amidst personal setbacks, failures, frustra-

tions, and a demanding and stubbornly uncooperative world. People do the

best they can in an effort to make sense of their lives in time, even under

trying circumstances.


Culture, then, provides each person with an extensive menu of stories

about how to live, and each person chooses from the menu (McAdams,

2006b). Because different people within a given culture have different

experiences and opportunities, no two people get exactly the same menu.

Furthermore, a person cannot eat everything on the menu, so narrative

choices spell out a person’s relationship to culture. When the food comes

from the kitchen, people doctor it to their own tastes. They add pepper and

salt; they mix things up and throw some things away; they nibble from

somebody else’s plate; they may even send the order back and ask to see the

menu again. This is to say that individuals select and appropriate in the

making of narrative identity. They choose from competing stories, rejecting

many others, and they modify the stories they choose to fit their own unique

life, guided by the unique circumstances of their social, political, and eco-

nomic worlds, by their family backgrounds and educational experiences,

and by their dispositional traits and characteristic adaptations. A person

constructs a narrative identity by appropriating stories from culture. Self

and culture come to terms with each other through the narrative.

In conclusion, a narrative approach to understanding lives in time com-

plements prevailing models of the human life course that posit either (1)

developmental stages (e.g., Levinson, 1978), (2) life trajectories (e.g., Elder,

1995), or (3) personality traits (e.g., McCrae & Costa, 1990), while calling

into question the extent to which any model of the life course is itself

something of a narrative convention. In describing the life course in terms of

predictable passages and transitions, contextualized life trajectories, or sta-

ble dispositional traits, social scientists are prioritizing certain stories about

life over others. Yet, the imposition of one kind of narrative frame over

others is probably an inescapable feature of theory-making in the social

sciences. Indeed, some general stories about the life course may have more

scientific validity than others. Data can be garnered to support many dif-

ferent models of the life course, including those outlining developmental

stages, contextualized life trajectories, and stable dispositional traits. One

advantage, however, of narrative models for the life course is that they

prioritize the individual’s own construction of lives in time, rather

than projecting the construction offered by theorists themselves. Of course,

narrative approaches themselves are a projection of theorists’ preferences.

In prioritizing the story over other forms of human expression, narrative

approaches assume that storytelling is a natural, even universal, human

tendency, and that it represents a dominant way in which people make sense

of their lives in time (Bruner, 1990). Reasonable people may wish to take

exception to that assumption.

DAN P. McADAMS252

Narrative approaches focus attention on the meanings that people make

as they live their lives over time and try, over time, to make sense of them.

And narrative approaches show that those meanings are more than either

the idiosyncratic tales of self-contained individuals or the passive recitations

of society’s dominant narrative forms. Instead, life stories represent the

creative, contested, and constantly evolving interplay between a storytelling

agent and a complexly structured and storied world. As lives play out over

time, stories ultimately emerge, narrated in fits and starts by a compromised

but determined narrator who doubles as the protagonist of the tale. The

author may feel that it is his own story to tell. But even in the most heroic

tales, authorship is always joint – a project shared by the narrator himself

and the world wherein his story is told.

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DAN P. McADAMS258

PART IV:

METHODOLOGICAL

INNOVATIONS

259


260

LIFE COURSE ANALYSIS:

TWO (COMPLEMENTARY)

CULTURES? SOME REFLECTIONS

WITH EXAMPLES FROM THE

ANALYSIS OF THE TRANSITION

TO ADULTHOOD

Francesco C. Billari

1. LIFE COURSE ANALYSIS IN DEMOGRAPHY

The life course approach as an interdisciplinary program of study has been

under development since the mid-1970s (Mayer & Tuma, 1990). The idea of

studying the unfolding of individual lives within their local and broader

context has unavoidably brought life course scholars to emphasize

complexity rather than simplicity. In their end-of-millennium review, Giele

and Elder (1998) identify four chief elements as fundamentally shaping life

courses: individual development (human agency), history and culture

(location in time and place), social relations (linked lives). The intersection

of such elements constitutes the fourth chief element: the timing of lives. In

the life course approach, a set of interconnected trajectories lies at the heart

of the analysis; trajectories are themselves shaped by events.





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10010-0

261

Demographers who are familiar with the life course approach are aware

that the key elements outlined by Giele and Elder are naturally linked to

notions used to define the coordinates of populations in demography. The

human agency concept lies behind the use of age as a privileged time axis.

Location in time and, to an extent, the idea of linked lives suggests cohorts

or groups of cohorts as basic descriptive units for comparison. History and

culture emphasize the importance of period and location in space. As events

lie at the very heart of the tradition of demography, it is not a surprise that

the life course approach has been particularly influential in demographic

research (Dykstra & van Wissen, 1999).

Large-scale surveys collecting individual-level retrospective demographic

history, starting from the World Fertility Survey (WFS) have given the

biggest impetus to collect in most demographic surveys data on the timing

of events in relevant trajectories (Hobcraft & Murphy, 1986). Elsewhere

(Billari, 2003), life course analysis has been defined as the statistical analysis

of data on the timing of events (when do events happen?), their sequencing

(in which order do events happen?), and their quantum (how many events

happen?). Ideally, life course analysis includes the possibility to analyze the

timing, sequencing, and quantum of events as depending on the elements

mentioned by Giele and Elder: individual-level human development, social

relations, location in time and place.

A progressively increasing complexity has shaped – as we shall see in more

detail in this paper – methods for the analysis of life courses (Billari, 2003).

While scholars who advocate a wider use of qualitative research see their

approach as a way to cope with the complexity of life courses, those who,

like myself, feel more comfortable with an emphasis on quantitative research

have more and more moved to complex methods as well. Complexity comes

at some costs, but it also brings some paybacks. We shall try to disentangle

costs and payback by looking at a specific field of study: the transition to

adulthood. The study of the transition to adulthood has been a primary

topic for life course scholars, and it has greatly benefited from advances in

the life course approach and in life course analysis. The need for method-

ological advances in this area comes from the peculiar ‘‘demographic den-

sity’’ of early adulthood: in advanced societies several events happen during

this period, and diversity between individual lives becomes importantly vis-

ible (Rindfuss, 1991). Processes that have usually been studied using a life

course approach include leaving the parental home, starting and/or dis-

solving a union, having a child, migration, job entry and exit, retirement.

In this paper I argue that there are two main approaches to life course

analysis (and in particular the analysis of demographic life courses), and

FRANCESCO C. BILLARI262

that these two approaches serve complementary, and equally necessary,

aims. The first is the event-based approach, based in general on event history

analysis; the latter set of techniques has become one of the basic ingredients

of advanced methodological courses in population sciences during the last

decades of the twentieth century. Connected to this approach, I review

developments from the econometric literature on program evaluation, which

aims at discovering causal relationships within life courses and appears still

to not have strongly influenced life course research. The event-based ap-

proach is mainly targeted at the explanation of life courses, more specifically

at the decomposition of the life course picture into the raw materials

(in particular, events) that constitute it, and in the search for the causes that

underlie the timing of these events. The second is the holistic approach,

mostly relying on sequence analysis, which has had a less significant impact

on life course research with respect to the event-based approach. This sec-

ond approach is mainly targeted at what we may define complex description

of life courses, or at the construction of what sociologists will define ‘‘ideal-

types’’ of trajectories. Looking at life courses as conceptual units, analyses

based on the holistic approach aim at gazing the whole picture, in a complex

way that somehow parallels what can be obtained using qualitative analysis.

Both approaches have been supported and have become widespread with

the availability of corresponding specific software packages.

The paper is structured as follows. In Section 2, I start from analyzing a

debate between two cultures in statistical analysis and argue that the main

approaches in life course analysis are related to these two cultures. In

Section 3, I shortly review the ‘‘raw material’’, event-based approach, while

in Section 4, I review the ‘‘whole picture’’, holistic approach. Further re-

flections on the complementarity between the two approaches, future re-

search directions, and needs for data collection are discussed in Section 5.

2. TWO CULTURES? A DEBATE IN STATISTICS AND

ITS IMPLICATIONS FOR LIFE COURSE ANALYSIS

Life course analysis inevitably and heavily relies on statistics. It is thus

useful to look at the debate within the statistical community to better grasp

present and future directions in life course analysis. The idea that there are

‘‘two cultures’’ in statistical modeling has been launched in a controversial

paper by Breiman (2001), published in ‘‘Statistical Science’’. The paper is

followed by comments of other statisticians and by a rejoinder of the author.

Life Course Analysis: Two (Complementary) Cultures? 263

The two cultures to which Breiman refers are: (1) a ‘‘data modeling culture’’,

which is the mainstream in statistics, which assumes that ‘‘data are gener-

ated by a given stochastic data model’’; (2) an ‘‘algorithmic modeling cul-

ture’’, clearly favored by Breiman, which treats the data generation

mechanism as unknown. According to Breiman, the focus on data models

has ‘‘led to irrelevant theory and questionable scientific conclusions’’, ‘‘kept

statisticians from using more suitable algorithmic models’’, and ‘‘prevented

statisticians from working on exciting new problems’’. On the other hand,

algorithmic issues point to the ‘‘multiplicity of good models’’ – illustrated by

reference to the Japanese movie ‘‘Rashomon’’ to the conflict between sim-

plicity and (predictive) accuracy – the Occam razor issue, to the dimen-

sionality problem ‘‘curse or blessing?’’ In particular, these points relate to

the issue that prediction may be improved when using simultaneously more

than one model of the same data, and that using more complex models

improves prediction at the expense of interpretability (the latter being

in practical applications, according to Breiman, is a kind of second-

order need), and to the importance of adding dimensionality to problems.

Breiman’s thought has clearly been shaped by his experience as a statistical

consultant for businesses; it is clear that in this type of experience data have

to be taken as ‘‘given’’.

The most important comment – in terms of ‘‘culture’’ confrontation – on

Breiman’s paper is the one by Cox, which is in turn criticized in Breiman’s

rejoinder. Cox argues that the starting point is not ‘‘data’’ but ‘‘an issue, a

question or a scientific hypothesis’’ and that real scientific applications are

targeted at unraveling causal links using statistics, not only in form of the

analysis of collected data, but also in terms of data collection design. Model-

based statistical techniques provide, in his view, the best opportunity to

illuminate causality. It is clear that Cox’s thoughts have been shaped by the

interaction with the scientific community in the medical and social sciences

rather than with business or sciences involving very complex data (for ex-

ample, astronomy or genomics).

We can draw some lessons and inspiration from the Breiman–Cox debate

for what follows. The event-based approach to life course analysis is strictly

connected to what Breiman defines the ‘‘data modeling culture’’ – not by

chance is Cox one of the founders of modern event history analysis (Cox,

1972). Also, connected to the arguments of Cox, and differently from stat-

isticians who specialize in data analysis, scientists interested in the study of

the life course do not necessarily have to take data as given, and sometimes

they can design a study in view of unraveling the cause(s) behind the timing

of a certain event. Scientific hypotheses may take the shape of a parametric


or semi-parametric statistical model, and statistical techniques may help us

in comparing the goodness-of-fit of such models. For instance, in the case of

models for the age at first marriage or first union, theory-based parametric

formulation of the hazard rate have been described and can possibly be

compared. As we shall see more in detail later in this paper, the statistical

significance of the impact of covariates on hazard rates can be tested using

standard approaches, and this has been extensively used in event history

analysis looking for ‘‘causal’’ approaches (see Blossfeld & Rohwer, 2002).

Nevertheless, the algorithmic culture defended by Breiman has also some-

thing to contribute in the field of life course analysis. First of all, it is not

always possible in this field to design a study in view of performing a specific

study. An important example is the study of archival data, collected for

administrative needs, and used by historical demographers. One can say

without doubt that analyses of archival data have contributed greatly to the

analysis of life courses. Second, as the life course is a complex set of in-

terrelated trajectories, disentangling causal links may not be the only aim;

more specifically, algorithmic models can contribute greatly to the analysis

of life courses as whole conceptual units. A very similar argument to the one

illustrated here is presented by Ritschard and Oris (in this volume), who

distinguish between a ‘‘statistical modeling’’ and a ‘‘data mining’’ approach

to the analysis of life course data in demography – it is not by chance that

they find this important distinction starting from issues raised in historical

demography, a field in which the use of archival data has allowed to reach

important scientific knowledge.

These considerations justify the view that is taken in the remainder of this

paper: both approaches are useful, in a complementary way. In other words,

would we only ‘‘adopt’’ one of the two approaches (or the underlying sta-

tistical culture), we will know less about life courses. They are of different

importance for different problems, and a review and reflection on these

situations is necessary. Let us consider them in turn, starting with the event-

based approach.

3. THE EVENT-BASED, OR ‘‘CAUSALITY’’, CULTURE

3.1. Event History Analysis: from the ‘‘Causal Approach’’ to

‘‘Multilevel and Multiprocess Modeling’’

The set of statistical techniques that is now broadly defined as event history

analysis has become since the 1980s as one of the principal toolkits of


demography and life course research in general (Allison, 1984; Yamaguchi,

1991; Courgeau & Lelievre, 1992; Petersen, 1995; Blossfeld, Hamerle, &

Mayer, 1989; Blossfeld & Rohwer, 2002; Wu, 2003). More ambitiously, the

diffusion of this approach has been linked to a change of paradigm in

demography by Courgeau and Lelievre (1996), and as a solution to the

problem of search for causality in the social sciences (Eerola, 1994; Petersen,

1995; Bocquier, 1996; Blossfeld, 1996; Blossfeld & Rohwer, 2002).

Event history analysis generalizes life-table and standardization tech-

niques that have been extensively used in twentieth-century demography.

The statistical models of event history analysis usually aim to model indi-

vidual-level data collected from sample surveys or population registers.

Event history analysis focuses on the time-to-event as the dependent var-

iable, and since its early applications in demography it has been applied to

several types of events in the study of mortality, migration, family forma-

tion, and dissolution (Hobcraft & Murphy, 1986).

Besides the possibility to describe and compare life courses, the statistical

models of event history analysis have contributed to the explanation of life

course dynamics by linking time-to-event with explanatory variables

(covariates). Covariates can be external to a trajectory (as is the case for

macro-level variables, period effects), or internal to a trajectory (as is the

case of other trajectories of the life course that are potentially influencing the

time-to-event in a trajectory of primary interest). The difference between

external and internal covariates is relevant when one aims at giving a causal

interpretation of the impact of changes of the covariates on the hazard rate

of a given phenomenon.

External covariates can be classified into three main categories. First,

some covariates that are fixed during a life course or starting from a par-

ticular point of the life course, and are time-constant (gender, race, cohort,

age at marriage when studying time-to-divorce). Second, other covariates

have a temporal development that cannot be influenced by the process of

interest (age of an individual in the case of time-to-divorce). Third, some

covariates are located at an aggregate-level (‘‘macro-level’’) or proxy the

social dynamics (period, regional economic indicators, policy indicators). In

that sense, age-period-cohort models can be reconstructed at the individual

level. Multilevel event history models have been developed, taking advan-

tage of the improvement of software packages, at the end of the 1990s. They

allow the explicit modeling of the case of individuals who are aggregated in

household or regional clusters (see e.g. Barber, Murphy, Axinn, & Maples,

2000). As such covariates are ‘‘exogenous’’ – to use an econometric term – a

causal interpretation could be justified. This would in principle allow the


identification of causal effects in many types of life courses analyses. It is

however questionable that time-constant covariates such as race or gender

could be seen as causal factors in the timing of an event. The same is true

about age – in fact age is usually recognized as a proxy for other causal

factors that are unmeasured (Settersten & Mayer, 1997). Also in the case of

macro-level factors, it is often the case that they can be seen as ‘‘causing’’ the

timing of an event – nevertheless they can be considered triggering factors in

what could be defined a ‘‘situational mechanism’’ (Hedstrom & Swedberg,

1998), as they represent the situation (‘‘location in time and place’’ to use

Giele and Elder definition) in which a life course is unfolding.

Internal covariates usually refer to other trajectories of the same individ-

ual or of linked individuals, and their use allows researchers to study the

very complex interdependencies between trajectories that go to the very core

of the life course approach. Event history analysis may take into account

unobserved factors underlying these complex interdependencies, such as

unobserved value orientations or attitudes. There have been different ap-

proaches in the literature of the 1990s, with respect to the focus on the

relevance of time-constant unobserved factors for the analysis of parallel

and potentially interdependent trajectories (Wu, 2003). The so-called causal

approach (Blossfeld & Rohwer, 2002), assumes that all factors that are rel-

evant to the simultaneous analysis of several trajectories are observed and

included in the past history of the trajectories. This assumption, however, is

not completely explicit in the illustration of the proponents of the ‘‘causal

approach’’ or in papers using that approach.

Other modeling approaches, especially in the econometric literature,

allow for the inclusion in event history models of the impact of time-

constant unobserved factors. The most general applied approach to this

second type of strategy has been developed in the multilevel and multiprocess

modeling of life courses (Lillard, 1993; Lillard & Panis, 2000). These models

constitute in fact a generalization of the ‘‘causal approach’’, which has been

often neglected by scholars looking for the causal impact of changes in one

life course trajectory on other life course trajectories. The ‘‘multilevel and

multiprocess’’ modeling approach proposed by Lillard and Panis aims at the

simultaneous evaluation of the (1) impact of changing covariates (including

those related to other life course trajectories and thus to be considered as

internal covariates) on the hazard of experiencing an event; and (2) possible

common, time-constant, factors that influence a set of hazard rates. The first

aim is the same of the ‘‘causal approach’’, but it is reinforced by the fact that

in multilevel and multiprocess hazard models it is possible to control for the

common factors included in the second aim. In statistical terms, it is always


possible to test whether the general multilevel and multiprocess hazard

model can be safely restricted to the model following the causal approach –

this is done by testing whether the correlation of the unobserved factors that

influence a set of processes is zero. In other words, the causal approach

hypothesizes that there are no common and unobserved factors that affect

the set of processes under study.

The importance of taking into account the potential role of common

factors affecting life course trajectories can be described by one example.

Le Goff (2002) analyzed the transition to first marriage and first birth

among cohabiting couples in France and West Germany, by using a Lillard-

type model with a system of hazard equations with possibly correlated un-

observed heterogeneity. This could be contrasted for instance to Blossfeld

and Mills (2001), who analyze the same problem without allowing for

potentially correlated unobserved factors affecting life course trajectories,

but who explicitly refer in the title of their paper to the ‘‘causal approach

to interrelated family events’’. We focus on Le Goff’s results for West

Germany, for which the positive correlation between factors affecting entry

into marriage and into parenthood is statistically significant (+0.5455). In

Table 1, we show the results of the multiprocess model (Model (a)) and by

comparison those of the model without unobserved heterogeneity and pos-

sible correlation between unobserved factors (Model (b)). We see that not

taking the correlation into account leads to important biases in the estima-

tion of the impact of getting married on the transition to first birth (the bias

is up to 148% and never lower than 41%). More specifically, the ‘‘causal’’

accelerating impact of marriage is exaggerated if one does not take into

Table 1. Impact of Getting Married on the Log-Hazard of Conception

for Cohabitants: Models Taking (a) and Not Taking (b) into Account the

Correlation between Factors Leading to Marriage and First Birth.

Model (a) Model (b)

At marriage After 1 year At marriage After 1 year

Cohort (1953–1961) 0.2935 0.4505 0.7268 0.9208

Cohort (1962–1974) 0.4836 0.866 0.9726 1.2233

Note: Other covariates are included in the model (see Le Goff, 2002, Appendix). The estimated

correlation between unobserved factors simultaneously affecting marriage and first birth (in

Model (a)) is 0.5455.

Source: Own elaboration on estimates by Le Goff (2002).


account that the propensity to marry and to have a child are highly inter-

related, and this is due to common factors that are often unobserved.

3.2. The (Causal) Impact of Events: Ideas from

the Program Evaluation Literature

Unraveling causal links controlling for observed and unobserved spurious

factors is the primary focus of another approach, which originates from the

– mostly econometric – literature focusing on program evaluation from ob-

servational data. In program evaluation, the main task is to estimate the

impact of participating in a certain program (usually, a labor market pro-

gram), the treatment, on a specific outcome. This estimate is used as a

support for policy decisions. Two key issues arise. First, to illuminate pol-

icies, one wants to isolate the causal impact of a certain program from other

effects that link the program with the outcome. Second, because for cost–

benefit evaluation the size of the causal impact, and not only its direction or

statistical significance, matters, the emphasis on minimizing estimation bias

is usually stronger than in traditional life course analysis.

The characteristic problem in program evaluation is that participation in

a program is voluntary and can be due to factors that are themselves cor-

related with outcomes. Thus, we face a similar situation when we want to

evaluate the impact on some outcome measure of life course events. Life

course events are subject to choice, and factors leading to choices may be

influencing the outcome as well. With respect to the transition to adulthood,

this scenario applies to several types of substantive problems, and some

studies have already used approaches originating from the evaluation

literature to study the transition to adulthood and its implications. First, we

may be interested in evaluating the causal impact of events (i.e. timing or

sequencing) in the transition to adulthood on the subsequent pathways to

adulthood; a typical question is whether teenage childbearing influence

subsequent educational or labor outcomes during early adulthood (see e.g.

Hotz, Mullin, & Sanders, 1997), or, as in the traditional program evaluation

literature, whether participation in a certain welfare program affects sub-

sequent labor outcomes (see Dehejia & Wahba, 1999). An alternative but

related issue is to study the long-term impact of choices during the transition

to adulthood on later years. Second, we may be interested in evaluating the

causal impact of events involving relevant others, in particular youths’ par-

ents, on the transition to adulthood; a typical question is whether parental

divorce has a causal impact on educational outcomes or family choices in


the transition to adulthood (see Painter & Levine, 2000). Third, we may

want to study the causal impact of pathways to adulthood on relevant

others; a typical question is whether out-of-wedlock births cause problems

to children; or, whether the leaving home of a child, and in particular the

transition to an ‘‘empty nest’’ has a causal impact on parental outcomes, for

example happiness (Mazzuco, 2003).

Although in some issues (i.e. welfare programs) experimental designs are

also feasible, we shall focus only on observational, non-experimental stud-

ies. The methodological literature on the issue, although relatively recent

(the origins can be traced to Heckman & Robb, 1985), has become vast (for

a non-technical review see, for instance, Bryson, Dorsett, & Purdon, 2002).

The basic ‘‘evaluation problem’’ or the ‘‘fundamental problem of causal

inference’’ (Holland, 1986) is that to truly know the effect of a certain event

(i.e. the participation into a program), we must compare the observed out-

come of an individual who has experienced the event of interest with the

outcome that would have resulted had that person not experienced the

event. Equivalently, we must compare the observed outcome of an individ-

ual who has not experienced the event of interest with the outcome that

would have resulted had that person experienced the event. This ‘‘counter-

factual’’ outcome cannot be observed, and all approaches developed in the

program evaluation literature aim at providing an estimate of the counter-

factual and at using this in order to identify the causal effect of an event.

In addition, the impact of an event can be different across different

groups of a population. For these reasons one needs to distinguish what is

called the average effect of treatment (what impact would the event have on

a randomly drawn individual) and the average effect of treatment on the

treated (what impact has the event had on individuals who have experienced

the event). The average effect of treatment indicates the average benefit were

the event to become compulsory (this is for instance interesting in the case of

welfare programs). The average effect of treatment on the treated indicates

the benefit, or cost of a specific event and is thus the parameter of main

interest in our field. For instance, it is the answer to questions like ‘‘how

different a teen mother’s subsequent Ys would be if she postponed or fore-

went the birth’’ (Hotz et al., 1997, p. 578). Hotz and colleagues explain why

they focus on the average effect of treatment on the treated in their study of

the effects of teenage childbearing by using two reasons. First, a pragmatic

reason: ‘‘this causal effect is more readily identified from available data than

are the causal effects applicable to the full population of women’’ (p. 579).

Second, a substantive, policy-oriented, reason: ‘‘policies that seek to reduce

the rate of teenage childbearing will likely target women who, under the


status quo, would become teenage mothers. Knowledge of a1 is sufficient to

assess the potential consequences of eliminating teenage childbearing for

these women’’ (p. 579).

We thus want to estimate the effect of treatment by controlling for spu-

rious dependence, possibly including dependence due to unobserved factors.

To estimate treatment effects from observational studies, there are three

approaches in the literature. First, one can use data on highly related in-

dividuals, usually twins, and exploiting the variation between them. This

approach has been used for instance by Kohler, Skytthe and Christensen

(2001) to estimate the impact of age at first birth on completed fertility.

Twin data however are not widely collected and the results are not always

easy to generalize. Second, one can use an instrumental variables (IV) ap-

proach, looking for variables that are good predictors of the event of

interest but are not related to the outcome measure. This approach is hardly

feasible when dealing with life courses, and the most promising avenue uses

IV to estimate bounds of causal effects, as in the case of the analysis of the

effects of teenage childbearing by Hotz et al. (1997). The third approach is

the most promising for life course research, propensity scores matching,

originally proposed by Rosenbaum and Rubin (1983), with the matching of

treated and untreated individuals according to observed covariates sum-

marized in a ‘‘propensity score’’.

We examine more in depth propensity score matching. It is basically a

two-step approach (although steps are relatively complex and some iteration

may be required). In the first ‘‘parametric’’ step, the ‘‘propensity score’’ is

estimated from a set of (possibly abundant) covariates that are supposed to

affect the probability that the event of interest (treatment) is experienced.

These covariates may also influence the outcome of interest (the outcome

also possibly being the probability to experience another event) but the

outcome is not used in the estimation of the propensity score. For instance,

one can use a probit or logit model with the probability of experiencing a

parental divorce as a function of a set of youth and family characteristics

(Painter & Levine, 2000 use this as a robustness check for their results). In

the second ‘‘nonparametric’’ stage, individuals who experienced the event of

interest (treated) are matched to individuals who have not experienced the

event of interest (untreated), according to the propensity scores estimated in

the first step. Matching is ideally similar to what is done with controlled

experiments, but different procedures are possible (for a practical review, see

Becker & Ichino, 2002). For instance, each treated individual could be

matched to the nearest untreated individual (nearest-neighbor matching)

but if treated individuals are fewer that untreated it is also possible to match


one treated case with more than one untreated case; in addition, untreated

cases may be matched with more that one treated case. It is however nec-

essary that treated and untreated have a large enough ‘‘common support’’ in

terms of comparability since treatment effects can be estimated only within

the common support. Using another approach, Rosenbaum and Rubin

(1984) propose to divide the treated and untreated group in a number of

groups (i.e. with the same score range), and matching occurs when the

groups of treated and untreated have similar covariates. After the matching,

outcomes are compared and tests of statistical significance of the difference

may be computed.

The use of matching on propensity score permits to reduce the bias due to

observed variables influencing both the probability of experiencing the event

of interest, and the outcome (Rosenbaum & Rubin, 1983). To control for

fixed unobserved characteristics influencing both the potential ‘‘cause’’ and

the outcome, it is necessary to use other techniques usually relying on lon-

gitudinal data. For instance, instead of the difference in outcomes between

treated and untreated one can estimate the difference-in-differences between

treated and untreated.

Let us recall the results of an example. Mazzuco (2003) uses propensity

score matching to evaluate the causal impact of the departure of the last co-

resident child on parental well-being, in terms of satisfaction and of self-

rated health. He uses data from the European Community Household Panel

and is thus able to use the difference-in-differences estimator: how much

does the transition into the empty nest stage affect a 1-year change of well-

being and self-rated health? Mazzuco compares a country characterized by

relatively early home-leaving (France) with a country characterized by ex-

ceptionally late home-leaving (Italy). We report his results in Table 2. They

show that the departure of the last child does not have a (causal) impact on

the well-being of fathers in both countries, and that it has opposite impacts

on mothers. French mothers have a higher satisfaction after the departure

of their last child, and their perceived health does not change. On the con-

trary, Italian mothers have a lower satisfaction and their self-rated health

worsens. For the substantive interpretation of results we refer to the paper

by Mazzuco.

The production of easy-to-use packages for estimating treatment effects

based on propensity score matching has and will have a crucial role in the

diffusion of the methodology. Recently, Becker and Ichino (2002) have

produced a set of Stata programs that allow building estimators based on

propensity score matching. This becomes also a good opportunity for

scholars interested in the life course.


4. THE ALGORITHMIC OR ‘‘HOLISTIC’’ CULTURE

Older notions of life course – for instance the concept of family life cycle –

were holistic, with more or less explicit reference to biological structures

(Settersten & Mayer, 1997). This has no longer been true in the life course

literature since the 1990s. Nevertheless, by focusing mainly on specific

events, with what Elder (1985) has called the ‘‘short-view in analytical scope’’

researchers may not grasp a unitary, holistic, perspective on life courses.

There are two main reasons to complement event-based analysis with a

holistic approach (Billari, 2001a). For the sake of simplicity, they may be

defined as the strong and the pragmatic perspective. From the strong per-

spective, present for instance in neo-classical economics, life courses are seen

subject to accurate inter-temporal planning, for instance as an outcome of

utility maximization. This has also led to study empirically long-term plans

in life courses and their consistency, as well as to critique on the realism of a

dynamic programming view of lives from experimental economics. For in-

stance, Keane and Wolpin (1997) in a paper on the career decisions of young

men estimate empirically a model that involves ‘‘the repeated numerical

solution of a discrete-choice, finite-horizon optimization problem, formu-

lated as a dynamic programming problem’’ (Keane & Wolpin, 1997, p. 476).

As life courses are assumed to be the (uncertain) outcome of planning, a

holistic perspective is thus hypothesized to be present in the behavior of

individuals themselves. In the social-demographic literature, the notion of

strategy has been emphasized. In the psychological literature, individual life

courses are assumed to be marked by internalized timetables. For theoretical

Table 2. The Comparative Impact of the Departure of the Last Child

(Transition into the ‘‘Empty-Nest’’) on (Co-Resident) Parents’

Satisfaction and Well-Being in France and Italy.

France Italy

Mothers Fathers Mothers Fathers

Satisfaction +0.407� 0.066 �0.568� �0.023

Self-Rated Health �0.005 0.059 �0.149� �0.013

Note: Estimates based on propensity score matching using data from the European Community

Household Panel (Mazzuco, 2003).�Statistically significant differences with po0.05. Satisfaction is measured with a sum of sat-

isfaction items ranging from 4 to 24, self-rated health ranges from 1 to 5.


approaches that assume that the individuals look holistically at their own

lives, it is undoubtedly necessary to have tools that allow to follow the same

perspective, and to analyze the life course as a whole.

From the pragmatic perspective, the life course as a conceptual unit is

thought of as being a contingent result of subsequent events. Following this

viewpoint, researchers should focus principally on events when they wish to

explain individual behavior (as discussed earlier). A holistic view is still

useful as a way to describe and to summarize the timing, sequencing, and

quantum of life course events. Comparative research across countries or

regions or across cohorts is one of the examples where the description of life

courses as whole conceptual units might provide particular insights.

Since the 1990s, Andrew Abbott introduced the social science to sequence

analysis (see Abbott & Tsay, 2000). In sequence analysis, each life course or

trajectory in the life course is represented as a string of characters (also

numerical). This representation is identical to the one used to code DNA

molecules in the biological sciences. Different analytical strategies to analyze

data arranged as sequences have been proposed. Optimal matching analysis

(OMA) is a method originally created for the alignment of DNA sequences.

The goal of OMA is to compute a matrix of dissimilarities between pairs of

sequences (thus, of life courses). This matrix can be used as the input for any

kind of statistical analysis requiring proximity data (e.g. cluster analysis or

multidimensional scaling). Applications in the sociology of occupations

have appeared since the 1990s, and a review of the potential applications in

the field of demography is provided in Billari (2001a), while a demographic

application on the transition to adulthood is discussed in Billari (2001b).

OMA has also specific drawbacks (Wu, 2000). The definition of distance

between states has an important influence on results, and thus it has to be

based on theoretical grounds. In addition, it can be difficult to understand

which variables in the definition of the obtained groups are more relevant

than others. Alternative approaches to the analysis of life courses as a con-

ceptual unit have also been discussed, although they have not yet had a large

impact on the literature. These include the use of correspondence analysis

(van der Heijden, 1987, Chapter 8) and data mining techniques (Billari,

Prskawetz, & Furnkranz, 2000).

As an example of the use of the algorithmic culture we discuss the analysis

of sequencing in the transition to adulthood among U.S. youth presented by

Mouw (2005). Mouw applies a monothetic divisive algorithm (MDA) to

create clusters of life courses that are internally as homogeneous as possible

and externally as heterogeneous as possible. The study follows the approach

suggested by Billari and Piccarreta (2001; 2005) in an analysis of the


transition to adulthood in Italy. As usual for sequence analysis, the coding

of variables is a key step. Mouw defines a sequence of dummy variables

called P (1 if an individual has left the parental home, 0 otherwise), E (1 if

an individual has left education, 0 otherwise), L (1 if an individual has

started working, 0 otherwise), M (1 if an individual has married, 0 other-

wise), B (1 if an individual has already become a parent, 0 otherwise). An

example for the coding of an individual life course is reported in Table 3.

Sequences are then processed by the MDA by choosing the splitting variable

that maximizes within-group homogeneity and between-group heterogene-

ity, according to a Gini heterogeneity index. It also allows the use of an R2

type of statistic to choose the optimal number of splits. As common in

algorithmic approaches, a tree representation is useful. We reproduce

(Fig. 1) the tree for U.S. women, which indicates that the most discrim-

inating circumstance is whether they had become mother by age 28 (to the

right of the tree, that is about two thirds of the sample). Six groups of

women’s life courses are found. Group 1 is characterized by delayed tran-

sition to motherhood and marriage. Group 2 is characterized by delayed

transition to motherhood, but marriage taking place at least by age 32.

Group 3 is characterized by having had birth by age 28, without having been

married by that age and having not begun work by age 33. Group 4 differs

from Group 3 by the experience of work by age 33. Group 5 is characterized

by a birth between age 24 and 28, and marriage by age 29. Group 6 is

characterized by earlier fertility (transition to motherhood by age 24) and a

transition to marriage by age 29.

5. CONCLUDING REMARKS

From the review we have made of the existing literature we can safely

conclude that no single approach is the ‘‘solution’’ to life course analysis. On

the one side, there are different approaches that are useful in specific cir-

cumstances within what we have defined the event-based, or ‘‘causality’’

culture – we have mentioned event history analysis and approaches based on

program evaluation, although in some cases the difference between the two

approaches is negligible or non-existent. On the other, by definition the

‘‘algorithmic’’ or holistic culture sees a multiplicity of ‘‘good’’ models as a

reasonable outcome. The two cultures prove to be more fruitful vis-a-vis

each other when answering to different questions? To put it simply, on one

hand, when we would like to ask ‘‘what would happen to the risk of ex-

periencing a certain event ify?’’, we shall probably use models rooted in the


Table 3. Coding of the Life Course of an Individual who has Left Home

at Age 23, Finished School at 25, Entered the Labor Force at 26, Got

Married at 30 and had His or Her First Child at Age 32.

22 23 24 25 26 27 28 29 30 31 32 33 34 35

P 0 1 1 1 1 1 1 1 1 1 1 1 1 1

E 0 0 0 1 1 1 1 1 1 1 1 1 1 1

L 0 0 0 0 1 1 1 1 1 1 1 1 1 1

M 0 0 0 0 0 0 0 0 1 1 1 1 1 1

B 0 0 0 0 0 0 0 0 0 0 1 1 1 1

Source: Mouw (2005).

All women

N=2,939

B28=1

N=1,968

B28=0N=971

M32=0N=465

Group 1 Group 2

M32=1N=506

L33=0 L33=1N=298

N=721 N=1,247

N=423 N=352B24=0

M29=0 M29=1

B24=1N=895

Group 3 Group 4 Group 5 Group 6

Fig. 1. Tree Representation of the Results of Applying a Monothetic Divisive

Algorithm to Data on the Transition of Adulthood of U.S. Women. B24 ¼ 1 and

B28 ¼ 1 indicate having become a mother respectively by age 24 and 28 (B24 ¼ 0

and B28 ¼ 0 indicate the opposite), M29 ¼ 1 and M32 ¼ 1 indicate having married

respectively by age 29 and 32 (M29 ¼ 0 and M32 ¼ 0 indicate the opposite),

L33 ¼ 1 indicates having worked by age 33 (L33 ¼ 0 indicates the opposite. Source:

Mouw (2005).


‘‘event-based’’ culture. On the other, when we would like to ask ‘‘what are

the key factors that differentiate life courses in their unity, and what are the

consequences of a broadly defined trajectory?’’, we shall probably use mod-

els rooted in the algorithmic culture.

However, also within the same study the two approaches can fruitfully

complement each other. For instance, Mouw (2005) uses the output of the

clustering procedure we illustrated in Section 5 as an input for a regression

analysis that is much more oriented towards causality, under the heading

‘‘Does the sequence matter?’’ Regression analyses show important differ-

ence in the risk of experiencing outcomes such as poverty at age 35, with

women in Group 3 with the worst outcome in a logit regression model (see

Table 4). Sequences are also found to influence subsequent happiness and

depression (women in Group 6 are the happiest and least likely to be de-

pressed). The same spirit of complementarity between the two cultures in-

spires the work of McVicar and Anyadike-Danes (2001) who use clusters

obtained starting from an optimal matching analysis of sequences to predict

successful and unsuccessful transitions from school to work.

It is necessary to mention some caveats in these concluding remarks. In

the ‘‘causality’’ culture we should refrain from the idea of finding the ‘‘true’’

impact of events in the life course, as is sometimes done in the econometric

literature – the impact of an event, if we go back to the conceptual frame-

work on the life course by Giele and Elder, depends on the role of relevant

others (‘‘linked lives’’) as well as on other contextual factors (‘‘location in

time and place’’). The search for causality in the life course is thus a never

ending story, and the most promising new analyses based on either

Table 4. The Effect of Sequences on Life Course Outcomes: Coefficients

Related to Group Membership as Reported in Fig. 1 in a Logit Model for

Poverty Status at Age 35 (other coefficients not reported here).

Coefficient Standard error

Group 1 (B28 ¼ 0; M32 ¼ 0) 0.810 0.549

Group 2 (B28 ¼ 0; M32 ¼ 1) �0.297 0.547

Group 3 (B28 ¼ 1; M29 ¼ 0; L33 ¼ 0) 1.352�� 0.255

Group 4 (B28 ¼ 1; M29 ¼ 0; L33 ¼ 1): reference

Group 5 (B28 ¼ 1; M29 ¼ 1; B24 ¼ 0) �0.411 0.432

Group 6 (B28 ¼ 1; M29 ¼ 1; B24 ¼ 1) �0.074 0.239

Source: Mouw (2005).��Statistically significant differences with po0.01.


simultaneous hazard models or on other types of program evaluation take

what we may define a multiplicity of causal impacts for a multiplicity of

contexts as a strong point in the analysis. The caveat for the algorithmic

culture is possibly a similar one – not always what has been developed in

other fields can be directly translated to the analysis of life courses, and a

fecund interaction is fundamental in order to obtain optimal results.

No approach can work without appropriate software, and here it is worth

mentioning that some scholars have sometime worked hard to help other

scholars implementing new approaches. Transition Data Analysis (TDA)

(Rohwer & Potter, 2003), applied Maximum Likelihood (aML) (Lillard &

Panis, 2000) have been explicitly developed to study life courses using haz-

ard models, with aML allowing for the inclusion of correlated unobserved

heterogeneity between processes. Commercial packages have and will be

used, with Stata playing a prominent role in the program evaluation ap-

proach. For what concerns the algorithmic approach, TDA is also providing

a toolkit for sequence analysis, including optimal matching analysis, while a

set of non-standard programs written by researchers for the moment is used

for other applications.

To conclude, data are crucial ingredients for our analyses. Breiman de-

fined what we have put in parallel to the event-based or causality culture as

‘‘data modeling culture’’. The sort of data that have been collected since the

1970s in demography included detailed retrospective reconstruction of life

course trajectories. For a while this seemed to be the final answer to the

search for causality (see, for instance, the claims of Blossfeld & Rohwer,

2002). Nevertheless, by emphasizing the importance of unobserved heter-

ogeneity and of the bias introduced when not taking it into account we

acknowledge that more work has to be done. Prospective panel studies,

aiming at observing what is unobservable in a retrospective study (e.g.

intentions, value orientations and social capital) will provide to the causal

approach a much better equipment. On the other hand, the algorithmic

approach is to be exploited when data that are not directly collected for

research purposes – such as in the case of administrative registers – are to be

analyzed. Much more work is still to be done, in different directions, to

progress in life course analysis.

ACKNOWLEDGMENTS

The author gratefully acknowledges for comments and suggestions the ed-

itors of this volume, the members of the Working Group on Transitions to


Adulthood in Developed Societies of the International Union for the Sci-

entific Study of Population (IUSSP), and Stefano Mazzuco. A presentation

based on these ideas was given at a lecture organized at the Department of

Sociology of the University of Calgary by Prof. Anne H. Gauthier. Finan-

cial support is acknowledged to the Department of Sociology of the Uni-

versity of Calgary, Universita Bocconi and MIUR.

NOTE

1. This is the symbol for the average effect of treatment on the treated.

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282

LIFE COURSE DATA IN

DEMOGRAPHY AND SOCIAL

SCIENCES: STATISTICAL AND

DATA-MINING APPROACHES$

Gilbert Ritschard and Michel Oris

1. FROM DEMOGRAPHIC ANALYSIS TO

LIFE COURSE APPROACH

This chapter has essentially a methodological purpose. It discusses recent

advances in statistical event history analysis and Markov models and pro-

motes the use of tools from the developing field of data mining, with special

attention to the discovering of characteristic sequences and induction trees.

Before turning to these methodological aspects, we begin here by explaining

why demographers have been relatively reluctant to implement the life

course paradigm and methods, while the quantitative focus and the concepts

of demographic analysis a priori favored such implementation. A real in-

tellectual crisis has been needed before demographers integrated the neces-

sity to face up the challenge of shifting ‘‘from structure to process, from

macro to micro, from analysis to synthesis, from certainty to uncertainty’’

$The funding for this research is from Swiss National Science Foundation, projects 1114-68113

and 100012-105478.





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10011-2

283

(Willekens, 1999, p. 26). This retrospective look also shows impressive

progresses to promote a real interdisciplinarity in population studies, knot-

ting the ties between demography and the social sciences.

Although demographic analysis has a long history (see Dupaquier &

Dupaquier, 1985), the methods still used today have essentially been elab-

orated between the mid-nineteenth and the mid-twentieth centuries in West-

ern societies that felt successively threatened by race degeneration, declining

birth rates, and ageing. The macro frame was that of the demographic

transition, i.e. the evolution from young populations with high fertility and

high (especially infant and child) mortality to ageing populations with net

reproduction below the threshold of generations’ renewal and a tremendous

increase in life expectancy at birth.

From a methodological point of view, a starting point in demographic

analysis has been the mortality table. It implies a dynamic perception of

population with entrances (births or in-migrations) and exits (deaths or out-

migrations), and the idea that other events (like migration) can censor a given

risk (like mortality). It rapidly constrained the conceptual distinction between

a generation – i.e. those who are born in the same year or period – and a

cohort – i.e. those who are experiencing an event (marriage for example) in

the same year or period. The mortality table also resulted not only in an

average age at death but also in a distribution of the risk along the life course,

providing a survival curve. Finally, the immediate comparison of these curves

among sexes, and even more among matrimonial statuses, revealed selection

processes, like those supporting the over-mortality of singles compared to

married people. Heterogeneity and differential frailty were not ignored. After

the generalization of mortality analysis (from mortality- to life tables), cer-

tainly no scientific discipline was better prepared for the life course methods

than demography. Nevertheless, the paradigms clearly diverged.

First, dealing with structures and flows, demography has been a science of

reconstruction and description of patterns and behaviors, through a well-

established quantitative methodology, and the conviction that higher the

number of observations, more accurate – and possibly useful – were the

results (for a typical example, see Vallin, 2001). Demography was a science of

the masses, growing or stagnating, young or old, but not of the individuals!

Second, the engagement of generations of scholars was largely motivated

by the central character of population issues and the location of demo-

graphy at a crossroad between economy, sociology, epidemiological studies,

territorial analysis, political sciences, and, more recently, cultural and

gender approaches. However, research and collaborations were in reality

highly segmented, with a clear tendency to specialization on a geographical

GILBERT RITSCHARD AND MICHEL ORIS284

and/or thematic basis (typically mortality, fertility, marriage and family

formation, or dissolution, migrations, structures, prospective). Among

many others, the last edition of the excellent Encyclopedia of Population

(Demeny & McNicoll, 2003) illustrates that propensity. Such segmentation

was clearly inscribed in the methods. In the estimations of mortality,

mobility was statistically treated as a censor but explicitly presented as a bias

for a ‘‘pure’’ analysis of mortality. Clearly, the approach consisted in stud-

ying a demographic behavior as independently as possible from the other

ones, without systemic perspectives.

Third, the demographic evolution made apparent the limits of the es-

tablished methodology. Both mortality and life tables can be calculated

longitudinally based on the observation of generations, or on cross-

sectional data, i.e. the observation of deaths by age classes at a given time

point – mixing thus generations with different history together. For the

simplest table, that of mortality, since expectation of life at birth now ex-

ceeds 80, the longitudinal approach implies a population reconstruction

from at least the 1920s, what is quite difficult, especially if we do not accept

the hypothesis of a null effect of migration. Inversely, all the statistical

offices of the developed countries collect the data for the calculation of

cross-sectional measures for a long time. However, can we accept the under-

lying hypothesis of continuity while the duration of life wins 1 year every

3–4 years and while we know that the – generational – age distribution of

those gains has drastically changed during the last decades? Similarly, while

in the context of the so-called ‘‘second demographic transition’’ there are so

many changes in the fertility calendar, do we have to constrain ourselves to

the observation of those generations who have finished their fertile life and

renounce to study the present with other indicators than those of the mo-

ment? What is today the rationale of detailing the access to marriage while

cohabitation is rising? How record an informal event like entrance in co-

habitation? Both data collection and analytical tools have been challenged

by recent changes in demographic behaviors and family dynamics (for a

more in-depth discussion of those issues, see Caselli, Vallin, & Wunsch,

2001).

A real intellectual crisis resulted from the hesitation about the status of

demography within the social sciences, as well as from the frustration

against segmentation and the deficiency of old methods. The conscience that

description, especially some quantification with a pretension of objectivity,

hid and diffused ideological visions about ‘‘good’’ or ‘‘optimal’’ populations

also grew (Greenhalgh, 1996; van de Kaa, 1996; Veron, 1993; Szreter, 1993;

Hogdson, 1988, 1991).

Life Course Data in Demography and Social Sciences 285

Among the many reactions, revisions, and re-examinations, new

approaches and new methods rapidly emerged. No significant use of the

life course statistical tools can be observed before the mid-1980s, while for

example Cox’s foundling paper is dated 1972. When they finally have been

integrated by demographers, the new methods found many uses. Probably

the most obvious progress they supported was to replace demography in its

family setting. Something that could seem very strange, but perfectly illus-

trates this assertion, is indeed the discovery, precisely in the 1980s, of an

almost complete absence of dialogue between demography and family

sociology. While family is the place where most of the demographic be-

haviors take place and, to some extent, are decided, ‘‘few textbooks on

population contain a chapter devoted to the demography of the family.

Where such chapter does exist, it is generally shorter and more superficial

than those that deal with fertility, mortality, nuptiality, and migration, or

with the dynamics of age structure’’ (Hohn, 1992, p. 3). In 1982, the In-

ternational Union for the Scientific Study of Population created an ad hoc

committee to develop its study, but even in 1992 the animators of this group

saw family demography as ‘‘a recent and relatively underdeveloped branch

of population studies’’ (Berquo & Xenos, 1992, p. 8).

Its development has been extraordinary in the last years. Francesco Billari

chapter in this volume provides a nice illustration of such a change, which is

part of a shift from macro to micro, from an emphasis on macro-economic

evolutions as the essential determinants of demographic ‘‘answers’’, to a

multi-causal – multivariate – approach of behaviors, a shift also from av-

erage results to a more detailed study of distributions. In a quantitative

discipline, major evolutions necessarily imply to take up technical challeng-

es. ‘‘The traditional demographic analysis of such events as births, mar-

riages, divorces, deaths, and migration has the advantage that number of

these events can be related to individuals in the same age group and can,

therefore, be measured more easily and included in models. The inclusion of

other family members in such analyses causes difficulties because they will

generally differ in age and sex, and complications are also introduced be-

cause they do not generally live together continuously’’ (Hohn, 1992, p. 3).

Although several attempts have been made to construct a ‘‘household

demography’’ (Van Imhoff, Kuijsten, Hooimeijer, & van Wissen, 1995), the

life course paradigm and its methodological individualism clearly imposed

themselves. Offering both concepts and statistical methods, it represents a

shift toward microanalysis of individual data and causal research that not

only deeply renews the discipline, but also provides the vocabulary for a new

interdisciplinarity, first within the social sciences, then beyond (Blossfeld &


Rohwer, 2002; Dykstra & van Wissen, 1999). The first substantial gain has

been the study of multiple events, marriage and first birth, or moving and

starting a new job for instance, a kind of investigation that also raises the

issue of event sequencing and interactions that is typically treated with event

history analysis. If people have several careers that they must make com-

patible, their life transitions also reflect socioeconomic constraints, cultural

norms (about the ‘‘proper’’ age, sex, or behavior), as well as compromises

between several individual aspirations within or beyond the domestic unit.

Through researches in this huge area, family demography made for sure

tremendous progress during the last 20 years.

However, the shift has been so sudden that globally the complexity of

causalities remains too often underestimated (see Courgeau & Lelievre,

1993; Blossfeld & Rohwer, 2002; Bocquier, 1996; Alter, 1998; Billari, 2005),

as well as several technical traps. The problem is essentially that when

studying a population of individuals observed along the time, since each life,

the product of complex and multiple interactions is, as a matter of fact,

unique. Hence, interpreting and generalizing from samples require several

cautions. In the next section, we recall the main event history regression

models and discuss the question of heterogeneity. We cannot consider that

the elaboration of indicators at an individual level about household, family,

and community contexts is enough to deal with the more and more raised

issue of ‘‘linked’’ or ‘‘interdependent’’ lives (Hagestad, 2003). We show the

interest of robust estimates and shared frailty in that perspective. In the

same section, we also present the Markovian models that are particularly

useful for the study of transitions within a set of states (matrimonial or

social status, for example) periodically observed. In the interdisciplinary

perspective, which is one of the life courses, we consider it important to go

beyond the simple transitions typically studied in demography (from single

to married, from the first to a possible second child, from life to death, and

so on), and to investigate how, from a starting position, a destination is

selected among several possible. While family dynamics and life courses are

more and more open, such investigations are essential to deal with the

characterization of transitions as ‘‘normal’’ or ‘‘non-normal’’ without falling

again in the trap of ideological reading (see, for example, the discussion in

Oris & Poulain (2003) about the stigmatization of early home leaving).

Indeed, we assess more globally that there is a deficit of research on

trajectories between aggregate descriptions and causal analysis. Regression

models attempt to quantify how a factor, measured by an indicator, affects a

risk. However, such results tell us nothing about the calendar and no more

about the alternatives to this risk in life courses. It is essential to look


carefully at transitions in trajectories to target properly a causal analysis,

and this step is clearly too often superficial, if not absent. Several methods,

recently developed or recently made available in statistical packages, offer

opportunities to fill this gap. Among them, we promote in Section 3 some

highly flexible heuristic tools from the developing field of data mining, es-

pecially mining event-sequential association rules, and induction trees that

seem to us the more promising for life course data analysis.

2. STATISTICAL MODELING OF LIFE EVENTS

Life course data are longitudinal in their essence. Here, we focus on events,

an event being the change of state of some discrete variable, e.g. the marital

status, the number of children, the job, or the place of residence. Such data

are collected mainly in two ways: as a collection of time-stamped events or

as state sequences. In the former case, each individual is described by a

collection of time-stamped events, i.e. the realization of each event of in-

terest (e.g. being married, birth of a child, end of job, moving) is mentioned

together with the time at which it occurred. In the latter case, the life events

of each individual are represented by the sequence of states of the variables

of interest. Panel data are special cases of state sequences where the states

are observed at periodic time. The first kind of data is typically analyzed

with event history regression methods, while methods for state-sequence

analysis like Markov transition models are best suited for the latter. We

briefly discuss hereafter the scope and limits of these approaches.

2.1. Event History Regression Models

When we have time-stamped events, the question of interest is the duration

of the spell between two successive events, or somewhat equivalently, the

hazard rate h(t) for the next event to occur precisely after a duration t, i.e.

the conditional probability for the event to occur at t knowing that it did not

occur before t. Longitudinal-regression models focus on this aspect. They

express either the duration or the hazard rate as a function of covariates. It

is worth mentioning that these models are also known as survival models,

especially in area like biomedicine and engineering where the event of con-

cern is just death or breakdown.

There are continuous-time models and discrete-time forms. With contin-

uous time, the main formulations (see Blossfeld, Hamerle, & Mayer, 1989;


Courgeau & Lelievre, 1993) are as a duration model or as a proportional-

hazards model. Duration models consider ln(T), the logarithm of the time to

the event, as a linear function of the explanatory factors. Proportional-

hazards models suppose that the ratio between the hazard for a given profile

(in terms of the covariates) and that for a reference baseline profile remains

constant over time and expresses the logarithm of this ratio (or proportion)

as a linear function of the covariates.

Duration models, also known as accelerated failure time models, assume

usually an exponential, Weibull, log-normal, log-logistic, or gamma distri-

bution for the duration T. The proportional-hazards model is compatible

with for instance, exponential, Weibull, and Gompertz duration distribu-

tions. It includes also the perhaps most widely used Cox (1972) semi-

parametric model that requires no assumptions on the form of the duration

distribution. Most statistical packages (SAS, S-Plus, Stata, R, TDA, etc.)

provide procedures for estimating such models. At least until version 13,

SPSS, however, offers only support for the Cox model.

Discrete-time models (see Allison, 1982; Yamaguchi, 1991) include the

proportional hazard-odds model, also owe to Cox (1972), the discrete pro-

portional-hazards model (Aranza–Ordaz, 1983), and the log-rate model

(Holford, 1980). In the first model, it is not just the hazard ratio, but the

ratio of the odds of the hazards that is supposed to be constant and having a

logarithm depending linearly on the covariates. The discrete proportional

hazards model expresses the log minus log of the complementary hazard as a

linear function of the covariates. The log-rate model on its side expresses the

log-hazard in terms of proper and interaction effects of categorical variables

and also possibly of their interactions with duration.

For the estimation of the proportional hazard-odds model, some as-

sumptions are usually required upon the baseline hazard-odds. Letting b0tbe the baseline log hazard-odds after a duration t, the most common as-

sumptions are that it remains constant with t, is linear in t (Gompertz), or is

linear in ln(t) (Weibull). With these assumptions, a proportional hazard-

odds model can, if we organize the data in a person–period form, simply be

estimated as a logistic regression. Hence, it can be estimated by any software

that proposes logistic regression. Likewise, a log-rate model can be esti-

mated with any log-linear model procedure that allows for weighted cell

frequencies. Indeed, the log-rate model is a log-linear model of the weighted

number of events occurring in a time interval, the weight being the inverse of

the population at risk in this interval. The fitting of a discrete proportional

hazards model requires the perhaps less frequently implemented procedures

for binary regression with a complementary log-log link.


A common issue with the time to event models is the handling of censored

data. Censored data occur when the observed start (left) and/or end (right)

time of a spell are not its actual start and end time. For instance, if we

observe job duration, some jobs may not be terminated at the time of the

survey and are hence right-censored. Though no event is recorded at the

end of the right-censored spells, these cases are taken into account by

entering the population at risk for job length lower or equal to the observed

duration.

Another issue is the handling of time-varying covariates. The solution is

quite straightforward in the discrete-time setting that works on person–time

data. For the continuous case, there are two major solutions: an ad hoc

extension of the Cox model that allows for discrete-time-varying covariate

and the episode-splitting approach (see Blossfeld & Rohwer, 2002 for de-

tails). Time-varying covariates offer a way to test and relax the somehow

strong proportionality assumption required by most hazard-rate models.

Indeed, this assumption implies the time independency of the ratio of haz-

ards of any two individuals, which clearly does not hold when the ratio

depends on a time-varying variable. It is common practice to check the

significance of the interaction of a supposed time-independent variable with

t or ln(t). A significant interaction would provide evidence against time

invariance (see Therneau & Grambsch, 2000 for other tests of proportional

hazards and more advanced developments of the Cox model).

This event-history modeling, especially the Cox proportional-hazards and

discrete-time proportional hazard-odds models, has become popular among

demographers. Together with other social science scientists, historical

demographers have to face issues like competing events (multiple destina-

tions), repeatable events, and interacting events. The first two can easily be

handled with a software like TDA (Rohwer & Potter, 2002) that supports

episodes defined by four parameters, namely the origin state, the start time,

the destination state, and the end time. The interaction between events,

marriage end, and first child, for instance, needs a simultaneous equation

approach that has been investigated by Lillard (1993), and is discussed more

in depth in Billari’s contribution to this volume.

2.1.1. Shared Heterogeneity and Multi-Level Modeling

A further issue of importance, shared heterogeneity, is concerned with the

sampling nature of the data. These are often clustered, i.e. the individual

data come from a selection of groups, parishes, or families for example. In

such cases, members of a same group share a same contextual framework

and it is then of primary importance to distinguish effects that hold at the


group level from those that work at the individual level. A very concrete

example is the study of orphans’ survival after father’s death by Beekink,

van Poppel, and Liefbroer (1999) for a 19th-century Dutch provincial town.

In the event-history file, initially each orphan was considered as a single

individual while there were not individuals but groups of siblings that en-

tered in the population at risk because of a shared event – dad’s death – and

supported this experience while sharing the same household context. Taking

into account the interrelatedness of the observations changed the results!

Along the same line, both in contemporary and historical demography, the

issue of the death clustering at the family level is a growing concern (Alter,

Oris, & Brostrom, 2001). All those studies extend the original discussion of

‘‘the impact of heterogeneity in individual frailty on the dynamics of mor-

tality’’ by Vaupel, Manton, and Stallard (1979).

To explain this aspect, let us consider the case of a simple linear regression

of the number of children on the education level in the presence of three

clusters like those depicted in Fig. 1, where the clusters are, let us say, three

villages. A simple regression on the whole data set is a straight line with a

positive slope, indicating that the number of children increases with edu-

cation. This effect clearly holds at the aggregated village level, i.e. the higher

the average education level in a village, the higher the average number of

children. This aggregated effect results despite the regression is fitted on

individual data. A separate regression on each cluster exhibits a negative

y = 3.2 + 0.2 x

y = 6.2 - 0.8 x

y = 15.6 - 0.8 x y = 12.5 - 0.8 x

0

1

2

3

4

5

6

7

8

9

1 3 5 7 9 11 13 15

Education

Ch

ild

ren

Fig. 1. Multi-Level: A Simple Example with Three Clusters.


slope in each of the three villages, indicating a negative effect of education

on the number of children at the individual level. Indeed similar misleading

results may appear when event-history regressions are fitted on clustered

data as illustrated by the examples discussed by Beekink et al. (1999) and

Alter et al. (2001). What are the solutions?

Table 1 summarizes alternative formulations that can be adopted when

we are in the presence of G groups. For simplicity, we consider here re-

gression models with c covariates, generalization to more complex models

like event-history models being straightforward. Model m1 will capture ef-

fects at the group level. In models m2–m4, differences between groups are

introduced by means of additional parameters, an approach that is suitable

as long as G is not too large. Model m2 fits separate models on each clusters,

while in m3, the regressions are only seemingly independent, since the var-

iance of the error term is supposed to be the same in each group. Model m4

corresponds to the well-known case where, for each group, a specific effect is

introduced as a dummy variable. For a large number of groups, random

effect models1 m5 and m6 are best suited. In these models, the regression

coefficients are allowed to vary randomly from one group to another. In the

shared frailty formulation, only the constant is random, while the other

coefficients remain the same for all groups. The main advantage of these

random effect formulations is that their number of parameters is, as can be

shown from the last column, independent of the number of clusters. Ran-

dom effect models m5 and m6 may, therefore, have a much lower number of

parameters than models m2–m4 when G is large.

Even if we are interested in the aggregated effect, estimating them with

individual data, as with model m1, for example, requires some caution.

Table 1. Alternative Linear Models in Presence of G Clusters g.

Model Constant Effect of

Covariate

Variance of

Error Term

Number of

Parameters

m1 Average model Same Same Same 1+c+1

m2 Independent Group specific Group specific Group specific G(1+c+1)

m3 Seemingly

independent

Group specific Group specific Same G(1+c)+1

m4 Dummies Group specific Same Same G+c+1

m5 Random effects Random across

groups

Random across

groups

Same 2(1+c)+1

m6 Shared frailty Random across

groups

Same Same 2+c+1


Indeed, the standard errors of the aggregated effects are derived from in-

dividual residuals, which may either over- or underestimate the between-

group discrepancy. For instance, in our example of Fig. 1, leaving out in

turn each of the three groups leads to great variations in the slope that

would be underestimated by the classical standard error. This aspect has

been investigated among others by Kish and Frankel (1974) and, for the

Cox model, for instance, by Lin and Wei (1989). In such settings, it is good

practice to use robust estimates of the variance of the regression coefficients.

Such robust estimates are usually obtained as grouped jackknife estimates,

i.e. by measuring the discrepancy of estimates obtained by leaving out suc-

cessively each of the G clusters, and can be expressed as sandwich estimates

(see Therneau & Grambsch, 2000, pp. 170–173).

Facilities for dealing with clusters are offered by several statistical systems,

Stata 8, S-Plus 6.2, and R 2.0 for instance. All the three mentioned programs

propose options to get robust standard errors. They also permit the intro-

duction of a shared frailty in parametric-hazard rate and Cox models. Com-

plete random effects are only available with discrete models that can be fitted

with logistic regression procedures. Indeed, logistic models are special cases

of generalized linear models (GLM).2 Hence, multilevel-logistic regression

is available whenever multilevel GLM is implemented. Barber, Murphy,

Axinn, and Maples (2000), for instance, show how to estimate a model with

several random effects with the HLM (Bryk, Raudenbush, & Congdon,

1996) and MLN (Goldstein et al., 1998) programs.

2.1.2. Illustration

To illustrate the scope of robust standard errors and shared frailty, we

consider a data set of 5,351 migrants collected from the 19th-century pop-

ulation registers of the Belgian commune of Sart (see Alter & Oris, 2000;

Alter et al., 2001, for a detailed description). This data set provides, among

others, information about the emigration date, the destination and the date

of return after emigration. Table 2 shows results of the fit of a continuous-

time Cox model. The hazard modeled is that of return after a time between 0

and 5 years, no return or return after 5 years being censored. We fitted a

basic model, i.e. without the cluster or frailty options, the same model but

requesting robust standard errors for the coefficients, and the model with a

gamma g(1/y,1/y) distributed frailty term shared by members of a same

family.3

The hazard ratios reported are just the exponential of the coefficients.

They indicate the hazard ratio for two profiles that differ by one unit of

the corresponding variable. For the frailty model this interpretation holds,


assuming the two profiles have the same frailty. For instance, according to

the basic model, the chances to return for a single are about one and a half

times the chances to return for a non-single. Likewise, the probability to

return is for a man about 3/4 of that for a woman. We checked on the basic

model that none of the time-covariate interactions is significant, which

comforts the proportionality assumption.

The coefficients are indeed the same for the basic and robust standard-

errors models. The significance of the coefficients differs, however, as can be

seen from the p-values. To be born in the Ardennes is significant at the 5%

level when we do not care about the cluster effect, while it is clearly not when

we control for it. This indicates that the seemingly significant birth-place

effect does not work at the family aggregated level. Likewise, we may notice

that, though the effect of the household economic ratio is significant among

families, its significance is not as clear as we would expect from the basic

model.

Let us now look at the results with a family shared frailty. First, we

may notice the highly significant variance of the random term, which

clearly indicates a between-families discrepancy. Two variables that looked

Table 2. Cox Model for Return within 5 Years after Emigration.

Coefficient Hazard Ratio p-Value (in %)

Basic Frailty Basic Frailty Basic Robust Frailty

Economic ratio 1.02 0.30 2.76 1.35 0.2 3.8 45.0

Man �0.28 �0.18 0.76 0.83 0.1 0.2 5.6

Single 0.40 0.52 1.49 1.68 1.2 1.2 0.3

Born in Ardennes 0.25 0.17 1.29 1.18 4.1 15.0 28.0

Age when leaving 0.01 0.00 1.01 1.00 12.0 17.0 62.0

To Ardennes Destination reference category

To rural �0.32 �0.60 0.73 0.55 5.7 14.0 0.2

To urban/indust �0.07 �0.23 0.93 0.79 50.0 68.0 6.8

To other �1.25 �1.25 0.29 0.29 0.0 0.0 0.0

Head or spouse of Parenthood reference category

Child of head 0.02 �0.25 1.02 0.78 89.0 90.0 19.0

Other parenthood 0.12 �0.27 1.13 0.76 54.0 56.0 26.0

No parenthood �0.50 �0.54 0.61 0.58 6.7 7.3 9.0

Standard deviationffiffiffi

yp

of family effect 1.75 0.0

Note: Sart 1812–1900, n ¼ 5,351.


significant become non-significant, namely the gender (man) and the eco-

nomic ratio. This is not surprising for the latter, which is a typical family

contextual factor shared by members of the same family. Gender, on the

other hand, is clearly an individual characteristic. Its lack of significance in

the frailty model seems to indicate that the effect is not systematic within the

families. Its overall significance follows probably from differences among

male and female singles. A reverse phenomenon is observed for the rural

destination effect that becomes significantly different from the reference

Ardennes in the frailty model.

2.2. Markov Transition Models

In the presence of state sequences in panel data form, the natural question is

what are the transition probabilities from the states at time t�1 to the

possible states at time t, and how are these probabilities affected by indi-

vidual histories or contextual characteristics. Homogenous-Markov models

assume that these probabilities are independent of time t. In first-order

models, the transitions are supposed to depend only upon the state at t�1,

which means that the first lag summarizes the whole history of states at t�1

and before. Models of higher order k consider that the transitions depend on

k lags, i.e. on the states at t�k,y,t�1. Thus, basic Markov models state

that the transition probabilities remain constant over time and depend on a

limited, usually small, set of previous states.

Markov models of order k generate, when we are in the presence of

s states, sk transition distributions, i.e. a huge number of probabilities. They

may be approximated by mixture transition distribution (MTD) models

(Raftery & Tavare, 1994; Berchtold, 2001; Berchtold & Raftery, 2002) that

involve a much lower number of parameters, which renders the models

easier to interpret.

Other extensions of the Markov model include the hidden Markov model

(HMM) (see Rabiner, 1989; MacDonald & Zucchini, 1997) in which the

successive states of the observed variables are only indirectly linked through

an unobserved Markov chain and the double chain Markov model

(DCMM) (Paliwal, 1993; Berchtold, 1999, 2002), which states that the ob-

served states are outcomes of a Markov process randomly selected by a

hidden process. The use of hidden processes is a way to relax the usually

strong homogeneity assumption. For example, when studying social mo-

bility with data covering a whole century, it is hardly defendable to assume

that the same process works during the whole period (Lynch, 1998, p. 96).


Despite their interest, there has been only a limited use of Markov mod-

els, especially of non-homogenous ones, by historians and demographers. A

search on ‘‘Markov’’ within the famous Population Index database4 results

in only 28 hits among thousands of references. Moreover, most of those 28

hits refer to working papers or highly focused articles (with an emphasis on

the study of multistate population dynamics). The main reason for such a

limited use is that standard statistical packages offer only limited facilities to

fit such models. The available tools require a heavy coding task that dis-

courages most potential users. We can expect, however, that Markov

modeling will become much more popular with the recent release of March 2

(Berchtold & Berchtold, 2004). This software offers a friendly way to es-

timate Markov models without writing down any line of code.

2.2.1. Illustration

To illustrate the nature of knowledge we can expect from such an analysis,

we consider here the Blossfeld and Rohwer (2002) sample of 600 job ep-

isodes extracted from the German Life History Study. The episodes have

been classified into three job-length categories: (1) p3 years, (2)43 and

p10 years, and (3)410 years, and the data reorganized into 162 individual

sequences of 2–9 job episodes, dropping the cases with a single episode. The

question considered is how the present episode length depends upon those of

the preceding jobs. Notice that the job-length sequences considered here are

not panel data, which demonstrates that Markovian models are not re-

stricted to panel data. In this setting, the subscript t refers simply to the

position in the sequence rather to a specific time period.

The first- and second-order homogenous transition matrices are given in

Table 3. The same table also gives the distribution of the independence

model in which the transition probabilities stay the same irrespective of the

previous job length. Let us briefly illustrate how these tables should be read.

The independence distribution implies that the overall probability for a new

job to be a short one is 50%, while this probability is 35% for a medium job

and 15% for a long job. The first-order matrix indicates that the probability

that a new job started after a short one has 57% chances to be again a short

job. This probability falls to 43% after a job of medium length and to 20%

after a long job. From the second-order matrix, it follows, for instance, that

this same probability is 55% when the preceding short job was itself pre-

ceded by a short one, 60% when the preceding short job followed a medium

job and 100% when the preceding short job followed a long job. The last

column in the tables gives the half-length of a conservative 95% confidence


interval for the probabilities in the concerned row. Hence, probabilities

smaller than this half-length should be considered as non-significant.

A glance at these tables leads to the following remarks. The first-order

matrix exhibits some differences in the transition probabilities after a short

(1), medium (2), or long (3) job. After a first job, the probability to start a

short job is significantly higher than to start a medium or long job, while

this is not the case after a medium or long job. The second-order matrix

does not provide evidence on the impact of the second lag job length. The

main differences concern the transition probabilities after long jobs (3),

which are mostly not statistically significant due to the low number of cases

concerned. This was confirmed by fitting an MTD model for which we

obtained a weight of 1 for the first lag and, hence, 0 for the second lag.

For relaxing the homogeneity assumption, we consider an HMM model

with a two-hidden-state process. Fitting this model, we get the distribution

of the initial state of the hidden variable, the transition matrix of the hidden

process, and the distributions of the transition to the job-length categories

associated to each of the two hidden states. These results are given in

Table 4. In addition, we get estimates (not shown here) of the most likely

sequence of hidden states associated to each observed sequence. Looking at

the cross tabulation below of these estimated hidden states with the ob-

served job length we see that the first hidden state is mainly associated to

Table 3. First and Second-Order Homogenous Markov Matrices.

Job Length at t Half Confidence

t–2 t–1 1 2 3 Interval

Independent 0.50 0.35 0.15 0.07

First Order 1 0.57 0.30 0.13 0.10

2 0.43 0.42 0.15 0.13

3 0.20 0.53 0.27 0.29

Second Order 1 1 0.55 0.30 0.15 0.11

2 1 0.60 0.30 0.10 0.20

3 1 1 0 0 0.65

1 2 0.37 0.45 0.18 0.18

2 2 0.50 0.41 0.09 0.20

3 2 0.45 0.33 0.22 0.38

1 3 0.33 0.17 0.50 0.46

2 3 0 0.87 0.13 0.40

3 3 1 0 0 1


short jobs and the second hidden state to medium and long jobs. This may

suggest considering only two types of jobs: p3 years and 43 years.

Hidden Observed

1 2 3

1 118 19 0

2 0 65 35

Table 5 summarizes goodness-of-fit statistics for our fitted models and for

the sake of comparison of the independence model. The shown statistics are

the number of independent parameters p, the deviance measured as minus

twice the log-likelihood5 (�2LogLik), the likelihood-ratio w2 statistics that

measures the improvement in �2LogLik over independence, its associated

degrees of freedom and its significance level, the pseudo R2 that gives the

relative improvement in �2LogLik and the Akaike (AIC) and Bayesian

(BIC) information criteria.6 These figures show that the fitted models do not

make much better than the independence model. We get the smallest

�2LogLik value for the second-order homogenous model, but at the cost of

11 additional independent parameters. The first-order homogenous model is

the only one that significantly improves the �2LogLik of the independence

model. It is also slightly better in terms of the AIC. However, no model

outperforms the independence model in terms of the BIC. These relatively

bad results are largely attributable here to the insufficient number of data

considered. This stresses a limitation of this Markov-modeling approach,

Table 4. Two State Hidden Markov Model.

Hidden State at Hidden State at t Half Confidence

t-1 t 1 2 Interval

Initial 0.56 0.44 0.11

1 0.78 0.22 0.12

2 0.53 0.47 0.19

Job Length at t

1 2 3

1 0.75 0.23 0.02 0.12

2 0.05 0.58 0.37 0.18


namely the complexity of the models in terms of number of estimated pa-

rameters that requires a very large number of data.

3. MINING LONGITUDINAL LIFE COURSE DATA

Despite the last decade great boost in the use of data-mining tools for the

knowledge discovery from data (KDD) in fields ranging from genetics to

finance, from marketing to medical diagnosing, from text analysis to image

or speech recognition, such approaches have received only little attention

for extracting interesting knowledge from longitudinal data describing life

courses. An important exception is Blockeel, Furnkranz, Prskawetz, and

Billari (2001) who showed how mining frequent itemsets may be used to

detect temporal changes in event-sequences frequency from the Austrian

FFS data. In Billari, Furnkranz, and Prskawetz (2000), three of the same

authors also experienced an induction-tree approach for exploring differ-

ences in Austrian and Italian life-event sequences. We initiated ourselves

(Oris, Ritschard, & Berchtold, 2003) social-mobility analysis with induction

trees.

Data mining is mainly concerned with the characterization of interesting

pattern, either per se (unsupervised learning) or for a classification or pre-

diction purpose (supervised learning). Unlike the statistical-modeling ap-

proach, it makes no assumptions about an underlying process generating the

data and proceeds mainly heuristically.

Beside their non-parametric or assumption-free characteristic, data-

mining methods present also the advantage for our social demographic

framework to be able to handle sequences of the various family, education,

work, health, emotional, and other personal events that define a life course.

They seem in that regard promising tools for gaining knowledge about life

trajectories and should thus usefully complement the previously discussed

Table 5. Global Model Goodness-of-Fit Statistics.

Model m p �2LogLik w2 df Sig BIC AIC Pseudo R2

Independent 2 472.8 0 0 — 483.7 476.8 0

Homogenous order 1 6 462.6 10.2 4 0.04 495.4 474.6 0.022

Homogenous order 2 13 460.6 12.2 11 0.35 531.7 486.6 0.026

HMM 2 states 7 468.6 4.2 5 0.52 506.9 482.6 0.009

Note: Number of sequences ¼ 107, usable n ¼ 237.


statistical methods. Event-history models, for example, focus on the risk of a

given transition, but do not provide insights on trajectories. Markov mod-

els, on the other hand, attempt to characterize the stochastic process that

drives successive transitions between states. They provide in that sense some

synthetic information about trajectories. However, only trajectories between

states of a generally unique variable, social, or civil status, for example, can

be investigated this way. Markov models, even those allowing for covari-

ates, can hardly handle together the various life events. Furthermore,

Markov models remain quite rigid by assuming that the transition prob-

abilities do not depend upon the present time but only on a small limited

number (the order of the model) of previous states. From a substantial

standpoint, the hereafter discussed sequence-mining approach is best suited

to discover among the many possible trajectories, for example, from the

diversity of formations to the diversity of working lives, those that are

typical of real life courses of real persons and by contrast those that are

atypical.

Since data-mining methods are mainly assumption-free, exploring trajec-

tories with them may answer to the criticisms of the French sociologist

Pierre Bourdieu (1986) about the ‘‘biographical delusion’’. Bourdieu, in fact,

denounced the concept of ‘‘life cycle’’, and its emphasis on norms, norms

supposed to lend to ‘‘normal’’ trajectories. With the assumption-free mining

of longitudinal data, we precisely pass the boarder between the ‘‘causality’’

or ‘‘data-modeling culture’’ and what Breiman (2001) calls the ‘‘algorithmic

culture’’ (see Billari, this volume).

In the rest of this section, we shortly describe the mining of sequential

rules and the induction tree approach, focusing on the nature of knowledge

we may expect from such tools (for a more general introduction to data

mining, see Hand, Mannila, & Smyth, 2001; or Han & Kamber, 2001).

These books cover many more methods. The two tools discussed here are,

however, in our mind, the two more promising ones for longitudinal data.

3.1. Mining Event-Sequential Association Rules

Each life course can be seen as a sequence of life events: birth, important

disease, recovering from disease, starting school, ending school, first job,

first union, leaving home, first child, death of father, marriage, etc. Mining

sequential-association rules aims at determining the most typical sequences

or subsequences together with their frequencies, and at deriving association

rules like having experienced the subsequence first job, first union, first child,


is most likely to be followed by a sequence marriage, second child. By

contrast, indeed, mining frequent sequences and rules also reveals atypical

life courses. Note that event sequences differ from state sequences as con-

sidered by Markov models or optimal matching. Nevertheless, sequence

mining could as well be applied to state sequences.

Technically, the mining of frequent-event sequences and sequential-

association rules is a special case of the mining of frequent itemsets and

association rules. In data mining, an itemset is a set of items that are selected

together and an association rule is just a rule that says that if A occurs then

B is very likely to occur too. The basic tuning parameters of the mining

process are the support and the confidence thresholds. The support is the

minimal frequency in the database for an item set to be selected, while the

confidence of the rule is the probability that the consequence occurs when

the premise is observed. These basic-selection criteria are complemented by

other additional measure of the interestingness of the rule, like the propor-

tion of the rule its counter examples. Most algorithms for seeking frequent-

itemsets and rules are variants of the well-known Apriori algorithm

(Agrawal & Srikant, 1994; Mannila, Toivonen, & Verkamo, 1994). A typ-

ical application consists in finding the items that are more often ordered

together by customers. Sequences that we consider here differ from general

itemsets in that order matters. Multiple algorithms adapted for sequences

have been proposed since the pioneering contributions by Agrawal and

Srikant (1995) and Mannila, Toivonen, and Verkamo (1997).

3.1.1. Illustration

We have not yet ourselves experienced a sequential rule mining analysis on

demographic data. For the sake of illustration, we report here the analysis

carried out by Blockeel et al. (2001). The data considered originated from

the 1995 Austrian Fertility and Family Survey (FFS). The events analyzed

are those of the partnership and fertility retrospective histories of 4,581

women and 1,539 men aged between 20 and 54 at the survey time. The

observed women and men were partitioned into 5 years cohorts and the

objective of the analysis was to discover frequent partnership and birth

event patterns that mostly varied among cohorts.

The mining was done by means of the Warmr process implemented in the

ACE Data-Mining System (Blockeel, Dehaspe, Ramon, & Struyf, 2004).

The search was not limited to simple sequences of strictly ordered events but

allowed for more complex patterns combining multiple subsequences. An

important pattern found was having a child after first union and having


both a marriage and a second child after this first birth, the marriage and

second child being not ordered. The seeking of such not strictly ordered

pattern requires indeed some filtering, namely the elimination of redundant

patterns. For example, completing the above mentioned pattern with the

additional condition of having a marriage after the first union would not

bring any new information and is therefore redundant. Also, the rules gen-

erated were restricted to premises refereeing to the cohort. Finally, only

patterns that exhibit a great discrepancy in the proportion of individuals

satisfying it in each cohort were retained.

Fig. 2 is an example of outcome provided by this analysis. It shows

the strong declining proportion of individuals who started their first union

when they married. The mining process found this pattern, i.e. date of first

union equals date of marriage, to be the one that exhibits the strongest

changes in frequency among cohorts. Indeed, many other patterns, some-

times more complicated, were also found to have great variability in their

frequency.

Fig. 2. Negative Trend in the Proportion of First Unions Starting at Marriage.

Source: Reproduced from Blockeel et al. (2001) with permission from the authors.


3.2. Social Transition Analysis with Induction Trees

Let us now turn to induction trees and the insight they may provide on the

understanding of mobility. In mobility analysis, the focus is on how states at

previous time t�1, t�2,y, and possibly some additional covariates, influ-

ence the present state at t. This setting is very similar to that of Markovian

models. In contrast with this parametric-modeling approach, the tree in-

duction is, however, a non-parametric method. It provides a heuristic way to

catch how the previous states and covariates jointly influence the state at t.

Though we focus here on intergenerational social-mobility analysis, it is

worth mentioning that the scope of induction trees for life course analysis is

much broader. For instance, De Rose and Pallara (1997) used a tree

approach for segmenting time to marriage curves of Italian women; Billari

et al. (2000) used trees for analyzing differences in event sequences between

Austrians and Italians; and we can easily imagine many other applications.

Induction trees, i.e. decision trees induced from data, are basically su-

pervised classification tools (Quinlan, 1986). As pointed out in Ritschard

and Zighed (2003), they also convey powerful descriptive information. Their

learning principle is quite simple and they produce easily interpretable

results.

An induced tree defines rules for predicting the value of a response var-

iable from a set of potential predictors. The set of rules indeed characterizes

a partition of the cases, each rule defining a class. The prediction inside each

class of this partition is simply the modal-observed value when the response

is categorical and the mean observed value when it is quantitative. In the

quantitative case, the tree is called a regression tree (Breiman, Friedman,

Olshen, & Stone, 1984). Extension in this case includes model trees

(Malerba, Appice, Ceci, & Monopoli, 2002) and logistic model trees

(Landwehr, Hall, & Frank, 2003), which use a linear or logistic regression

for the prediction inside the classes of the partition. Tree algorithms have

also been proposed for predicting functions instead of values and those that

like RECPAM (Ciampi, Hogg, McKinney, & Thiffault, 1988) predict, for

instance, survival functions may be of special interest for life course analysis.

Here we consider only categorical responses, i.e. classification trees. The

easiest way to describe the tree induction principle is by looking at an

example. We begin therefore by describing the framework of the illustration

we will consider.

We use social family history data on intergenerational-social transition in

the 19th-century Geneva (Ryczkowska & Ritschard, 2004). The data were

collected from the marriage-registration acts that provide the profession of


the spouses as well as that of their parents. For 572 acts, it has been pos-

sible to find a match with the marriage of the father of one of the spouses.

For these cases, we have the profession of the married man, of his father

at the son’s marriage, of the matched father at his own marriage, and of

the grandfather at the matched father marriage. The professions were

grouped into three social statuses, namely low, high, and clock and watch-

makers who formed an important specific corporation in the 19th century

Geneva.

The variable we want to predict is the status of the son at his marriage,

which is clearly a categorical response, and we consider four potential pre-

dictors. The first three are status variables, namely the status of the father at

son’s marriage, the status of the matched father at his own marriage, and the

status of the grandfather at father’s marriage. The fourth predictor is the

birthplace that can take one of the 12 values: Geneva city (GEcity); Geneva

surrounding land (GEland); neighboring France (neighbF); Vaud (VD);

which is a neighboring region of Geneva; Neuchatel (NE), a further

French-speaking region also specialized in watch and clock making, other

French-speaking Switzerland (otherFrCH), German-speaking Switzerland

(GermanCH), Italian-speaking Switzerland (TI), France (F), Germany (D),

Italy (I), and other. The grown tree is shown in Fig. 3.

The tree-growing principle is as follows. First, all cases are grouped to-

gether in a root node (at the top of the tree) in which the distribution of the

response variable, the status of the married man for our analysis, is its

marginal distribution. The goal is to split this group in new nodes such that

the distribution of the response variable differs as much as possible from one

node to the other. The splitting is done iteratively using the categorical

values of the predictor selected at each step. At the first step, we seek the

predictor that best splits the root node and split the node according to the

values of this predictor. The process is then repeated at each new node until

a stopping rule is reached. Stopping rules typically concern the minimal

node size, the maximal number of levels or the statistical significance of the

improvement in the optimized criterion. In our study, we have retained the

CHAID method (Kass, 1980) that selects at each step the predictor that,

when it is cross tabulated with the response variable, generates the most

significant independence w2 statistics.7 CHAID also seeks the aggrega-

tion level of the categories of the predictors that generates the most sig-

nificant w2 and then splits indeed according to the optimally merged

categories. We generated the tree of Fig. 3 with Answer Tree 3.1 (SPSS,

2001) by setting the minimal node size to 15 and requiring a maximal sig-

nificance level of 5%.


Fig. 3. Social Transition Tree with Birth Place Covariate.

Life

Course

Data

inDem

ographyandSocia

lScien

ces305

Alternative methods, among which CART proposed by Breiman et al.

(1984) and C4.5 due to Quinlan (1993) are among the best known, differ

mainly by the criteria used for selecting the split variable at each step.8

3.2.1. Knowledge Provided by the Tree

Looking at Fig. 3, we see that the first split is done according to the father

status at son’s marriage. This tells us that among the four attributes con-

sidered, the status of the father is the most discriminating. The status of the

married man depends, for instance, more on the father’s status than on his

birthplace. The distribution inside the nodes of the first level are just the

columns of the cross classification of the statuses of the father and the son.

We observe here that the clock makers form a much closed group with a

high probability for the son to become a clock maker when the father

himself is a clock maker while this probability is much lower for the three

other groups. A similar result holds for the high classes, while there are

evidences about social ascension possibilities when the father belongs to the

lower class.

Three of the four first-level nodes are split further. The only one that is

not split is that of married men whose father belongs to the clock and watch

makers. This node is thus a terminal leaf, which indicates that the status of

clock maker father conveys all the significant information for predicting the

status of the son. This is a consequence of the strong social reproduction

process inside the class of clock makers. The married men whose father was

dead are split according to the grandfather’s status, which means that the

grandfather’s status is more discriminating for this subgroup than the status

of the father at his own marriage. There is a strong tendency for the married

man to reproduce the grandfather’s status when the father is deceased. The

group defined by a high status of the father as well as that defined by a low

father’s status is split according to the birthplace. Both splits are binary.

They do not make use, however, of the same binary partition of birthplaces.

In both cases, i.e. with a father belonging to the low or high classes, the

men born in neighboring France, in German-speaking Switzerland, or in

Vaud have a relatively high probability to get only a low status. This is

also true for men born in French-speaking Switzerland outside Geneva and

Neuchatel when their father belongs to the lower class.

The additional levels show that when the high position of the father

results from a recent social ascension, i.e. ascension since the father’s

marriage (level 3) or from the position of the grandfather (level 4), the

reproduction of the father’s status by the married man is less strong. The

subtree that concerns the men whose father was dead, shows effects of


the grandfather’s status very similar to those of the status of the father when

he is alive at the marriage.

3.2.2. Goodness-of-Fit of the Descriptive Tree

Classically, the quality of a tree is evaluated in terms of its classification

predictive quality, which is measured by the correct classification rate of the

tree. Recall that the classification is done by assigning to each case the most

frequent value in its leaf. For our tree, the correct classification rate is 57.6%.

This corresponds to a 42.4% error rate. At the root node, before introducing

any predictor, the correct classification rate is 44.4%, which gives an error

rate of 55.6%. Our tree allows thus a 24% ( ¼ (55.6-42.4)/55.6) reduction of

the error rate. These figures are, nevertheless, irrelevant in our case, since we

are not using the tree for classification purposes. We do not consider the

classification results. The descriptive knowledge considered follows directly

from the distributions inside the nodes. Hence, we consider the tree as a

probability tree rather than a classification tree. In Ritschard and Zighed

(2003), we have proposed indicators that better suit this descriptive point of

view. We can, for instance, measure with a likelihood-ratio w2 (G2) the di-

vergence between the distributions predicted by the tree (those in the leaves)

and those of the finest partition that our four predictors may generate.9 We

get 312.5 for 300 degrees of freedom, and its p-value is 29.8% indicating

apparently a good fit. Note that though the four predictors define theoret-

ically 576 different profiles, only the 163 actually observed are taken into

account. When these profiles are cross tabulated with the three statuses, we

get 489 cells. For 572 data, this gives an average of a bit more than one per

cell, which is insufficient to ensure the w2 distribution of G2. Hence, we

should not attach here too much confidence to the p-value (Table 6).

For comparison purposes, Table 6 reports the G2 statistic for a set of

nested trees, namely the independence tree corresponding to the root node

only, the tree expanded respectively one level only, two levels and three

levels, the fitted tree and the saturated tree that generates the finest partition.

Beside G2, its degrees of freedom and significance level, the table shows the

BIC and AIC information criteria and the adjusted pseudo R2. The latter

measures the percentage of reduction of the G2=df ratio as compared with

the independence tree. The BIC and the AIC are G2’s penalized for the

complexity.

We see that with less than three levels there is a lack of fit, the divergence

with the finest partition being significant at the 5% level. The difference in

G2s between two nested trees can also be compared with a w2 distribution

with degrees of freedom being the difference in these degrees for the two


models. Thus, the Level 3 tree differs by DG2 ¼ 15:1 and Ddf ¼ 4 from the

fitted model, which is clearly significant. Hence, the two splits leading to

Level 4 look jointly statistically significant. From the BIC point of view, the

Level 2 tree provides the best compromise between fit and complexity. Level

3 or 4 trees seem, however, preferable according to the interesting insight

brought by the additional levels and the significant divergence of Level 2

with the saturated model. The AIC, which is known, however, to under-

estimate the impact of complexity, selects here the fitted tree.

Trees look really promising thanks mainly to their ease of use and to their

visual outcome. When it comes to interpretation, one should be aware,

nevertheless, that trees may be instable in the sense that small changes in the

data could alter the structure especially splits and variables selected at

higher levels. It is then important to avoid growing too complex trees. Re-

laying on BIC or AIC criteria should help determining a somewhat robust

tree. Splits behind the optimal BIC or AIC will be less reliable and their

interpretation then requires more caution.

4. CONCLUSION

This paper stressed the scope and limits of various methods available for

analyzing life course data globally, and especially in demography. Demo-

graphers and historical demographers invented their own longitudinal-

analytical tools like the life tables or the family reconstitution, almost since

the birth of their discipline. However, everywhere but probably more in the

French-speaking areas, those sciences of the masses hesitated to take a step

further, while they were so close from the life course perspective and meth-

ods. Many academics are still living this transitiony. For adepts of highly

quantitative social sciences, we wanted to both introduce and illustrate

Table 6. Goodness-of-Fit of the Tree and Subtrees.

Tree G2 Df sig BIC AIC Pseudo R2

Independent 482.3 324 0.000 2319.6 812.3 0

Level 1 408.2 318 0.000 1493.9 750.2 0.14

Level 2 356.0 310 0.037 1492.5 714.0 0.23

Level 3 327.6 304 0.168 1502.2 697.6 0.28

Fitted 312.5 300 0.298 1512.5 690.5 0.30

Saturated 0 0 1 3104.7 978.0 1


promising methodological perspectives without hiding the complexity of the

new approaches. At the same time, we did not elaborate this contribution

only for our disciplinary fellows, since one of the most important evolutions

is that the analytical techniques obviously lend us to neighboring disciplines

that share the same tools and explore similar concepts, giving to the inter-

disciplinary ambition a growing substance.

We have chosen to illustrate different approaches, and especially the

emerging data-mining techniques that should be able to provide original

additional insights on results provided by more classical statistical methods.

The discussion, however, is by no means exhaustive. Among the techni-

ques we did not discuss, optimal matching (Abbot & Forrest, 1986; Malo

& Munoz, 2003) deserves special attention. Optimal matching is, like

Markovian models, a state-sequence analysis tool. It is merely a data-mining

approach, since it proceeds heuristically. Unlike the mining of frequent se-

quences that does not care about the similarity between sequences, optimal

matching is concerned with the discovering of similarities between sequence

patterns. Optimal matching evaluates the proximity between two sequences

by seeking the minimal number of changes that can transform a sequence a

into a sequence b. Survival (Ciampi et al., 1988; Segal, 1988) and risk trees

(Leblanc & Crowley, 1992) developed in the field of biomedicine during the

first half of the 1990s would also merit further attention from historians and

demographers.

It is worth mentioning that the statistical and data-mining approaches are

not substitutes for one another. They are complementary, each method

bringing its own insight. The choice of a method will be dictated by the kind

of data available: spell durations, event sequences, state sequences, and

indeed the type of results expected: knowledge about probability of tran-

sitions, effects on these risks, characteristic trajectories, or life sequences.

Another important element for this choice, at least for the end user, is the

availability of user-friendly softwares and the level of expertise required to

run the method and interpret the results. Many softwares propose duration

or hazard models and/or classification trees. It is less obvious to find friendly

tools for Markovian models and the mining of sequential rules. March 2 is a

promising solution for Markovian models, while specialized softwares like

Clementine propose sequence mining tools (see http://www.kdnuggets.com

for a list of commercial and free data mining softwares). The use and in-

terpretation of hazard models is very similar to that of other regression-like

models, which renders them attractive. The interpretation of induction trees

is also very straightforward and looks therefore as a promising tool. Nev-

ertheless, the fine tuning of trees, which may be highly instable, requires


generally more care than hazard models. Mining frequent sequential pat-

terns also requires some experience to get interesting patterns. In any case,

the new highlights provided by these data-mining approaches are worth the

effort.

NOTES

1. Random effect models are also known as multilevel, hierarchical or mixed-effect models.2. GLM models (McCullagh & Nelder, 1989) cover a large number of parametric

models. They assume a distribution of the natural exponential family for the de-pendent variable and are, in their simpler form, simply characterized by a linkfunction that describes how the mean of the dependent variable is linked to the linearform of the explanatory variables. For example, we get the classical linear modelwith a Gaussian distribution and the identity link, the logistic model with a Bernoullidistribution and the logit link, and the log-linear model with a Poisson distributionand the log link.3. The estimations were obtained with S-plus 6.2. We suspect a bug in Stata 8 that

was not able to converge within 24 h for the frailty model while S-Plus provided theresults within 2 min.Formally, the estimated hazard model is h(t, x1,y, xp) ¼ ng h0(t) exp(b1x1+

y+bpxp), where h0(t) is the baseline hazard function and ng the shared frailty term.We estimated this model assuming a gamma g(1/y,1/y) distribution for the frailtyterm ng, for which we have E(ng) ¼ 1 and Var(ng) ¼ y.4. http://popindex.princeton.edu/5. The deviance -2LogLik may be seen as the distance between the predictions

generated by the model and the observed counts. Hence it is a measure of global fit.However, it cannot be used here to test the fit since we do not know its distribution.6. The AIC and BIC criteria are penalized forms of the –2LogLik that take ac-

count of the complexity, i.e. the number of estimated parameters. Among the two,the BIC is usually preferred since the AIC is known to insufficiently penalize com-plexity. The model with the minimal BIC offers the best compromise between fit andcomplexity.7. Significance is generally evaluated with a Bonferroni correction for taking ac-

count of the multiple test sequence that controls each split decision.8. CART maximizes the reduction in the Gini index also known as the quadratic

entropy. It generates only successive binary splits. C4.5 uses the gain ratio defined asthe reduction in Shannon’s entropy normalized by the entropy of the distributionamong the classes of the generated partition. Unlike the CHAID method, for whichthe significance of the w2 provides a natural validation for the split, CART and C4.5do not have such a natural split validation criteria. These methods complete there-fore the growing process with a post pruning round that, starting from the leaves,eliminates unreliable splits. Only splits that improve the predictive error rate areretained. There are also graph induction tools like SIPINA (Zighed & Rakotomalala,1996), which generalize trees by allowing the merge of nodes with similar insidedistribution.


9. G2 is indeed the deviance -2LogLik. It measures how far the counts predicted bythe tree are from those observed for the finest possible partition. When the predictedcounts are not too small, it has an approximate w2 distribution and can be used fortesting the goodness-of-fit. Note that the w2 reported in Table 5 would correspondhere to the difference between the G2 of the tree and that of the root node (inde-pendence). We expect w2 to be large while G2 should be small.

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FIVE STEPS IN LATENT CURVE

MODELING WITH LONGITUDINAL

LIFE-SPAN DATA

John J. McArdle

INTRODUCTION

During the past few decades, scientists from many disciplines have been

engaged in the study of human development over what is termed the ‘‘life-

span.’’ Of course, this term has early roots in the physical and biological

sciences. Life-span developmental research in psychology continues to pro-

mote the combination of (1) a paradigm shift in thinking about human

development as a process occurring over all ages in different ways, and (2)

the utility of a multivariate perspective on data collection and analysis (e.g.,

Baltes, Lindenberger, & Staudinger, 1998). The theoretical underpinnings

of life-span developmental psychology are outlined by Abeles (1987, p. 3):

Development is (1) a life-long process, (2)multi-dimensional, (3)multi-directional,

and (4) multi-determined. These kind of meta-theoretical principles have

become so embedded in the fiber of modern developmental research that

they are now hardly ever debated or even discussed. Instead, the current

issues focus on the best ways to formalize these ideas into practical multi-

variable research. The life-span research presented in this paper attempts to

add to this practice using contemporary statistical modeling analyses.





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10012-4

315

In some respects, the life-span view of developmental psychology is rel-

atively new (for overview, see Baltes & Nesselroade, 1979). This area

emerged as a separate discipline in the early to mid-1970s with contribu-

tions of a few key researchers. The Department of Psychology at West

Virginia University was a fertile source of much important work – at one

time, Warner Schaie was the head of this department, and Paul Baltes and

John Nesselroade were working together in it, first as assistant professors,

and then as associate professors. About a decade earlier, Schaie began his

longitudinal research by following the people from Paul Horst’s multivari-

ate study at the University of Washington (later termed the ‘‘Seattle

Longitudinal Study’’). Baltes, then a promising student of Ernst Boesch

(Saarbruecken), a Piaget Ph.D., was well-versed in the elegant theoreti-

cal work of Klaus Riegel, the founder of modern dialectic psychology.

Nesselroade was then a promising student of Raymond Cattell, the founder

of modern multivariate psychology and a major contributor to the fields of

cognition and personality.

This group stimulated debate and controversy in research on the sepa-

ration of Age (A) effects, Cohort (C) effects, and Time-Period (T) effects.

Schaie (1965) promoted a formal separation of ACT effects in and ANOVA

fashion using what he termed the ‘‘general developmental model.’’ Perhaps

surprisingly, Baltes was one of the first critics of the ACT approach (Baltes,

1968) – he suggested not to use Schaie’s model for explanatory purposes but

descriptive ones only. In this spirit, Baltes shifted the emphasis to analyses

of cohort-sequential and longitudinal sequences (see Schaie & Baltes, 1975).

Soon afterwards, Schaie’s work was debated vigorously by Cattell and his

students: e.g., ‘‘Rarely has such a Tower of Babel sprung up so quickly as in

this area of research.’’ (Cattell, 1970, p. 154; also Horn & Donaldson, 1976).

Of course, Cattell himself had investigated complex multivariate designs

before (e.g., Cattell, 1963, 1969), but it was Nesselroade who advocated the

inclusion of multivariate measurements, and he was soon active in the de-

bate as well (e.g., Baltes & Nesselroade, 1970).

As it turned out, these controversies on ACT methods were going on in

other areas of research, especially Sociology, and stimulated many others

and led to some of the most important contributions to life-span research.

There were immediate practical applications such as the ‘‘longitudinal and

cross-sectional sequences’’ in research by Baltes and Nesselroade (1972) on

West Virginia Junior High and High School students. These activities also

stimulated an important series of books on ‘‘life-span developmental

psychology’’ focusing on the integration of substance and method (e.g.,

Goulet & Baltes, 1970; Baltes, Reese, & Nesselroade, 1977; Baltes &

JOHN J. MCARDLE316

Nesselroade, 1979; Baltes, 1983). A large number of psychologists soon

followed the active lead of this small group from Morgantown, West

Virginia (afterwards at Penn State, now at the University of Virginia), and

the result became known as the ‘‘life-span movement in psychology’’ (see

Nesselroade, 2001).

The ACT controversy that stimulated this life-span research could be

considered just a natural extension of previous longitudinal considerations

(e.g., Wohlwill, 1973; Bell, 1954; McArdle & Bell, 2000). Also, subsequent

statistical studies proved that the general ACT problem was ‘‘not identified’’

and unique solutions were only possible using highly restricted models (for

review, see Horn & McArdle, 1980; McArdle & Anderson, 1990; Donaldson

& Horn, 1992). But, in retrospect, the survival and popularity of the life-

span movement can now be seen due in large part to a successful integration

of developmental theory and multivariate method. Clearly, the substantive

thinking was enhanced by the methodological thinking, and vice versa.

Most of these methodological debates concluded that the collection of

longitudinal data are a necessary ingredient for life-span research, and many

researchers have defined these issues in extensive detail, but most have

emphasized ‘‘the explanation of inter-individual differences (or similari-

ties) in intra-individual change patterns’’ (e.g., Wohlwill, 1973; Baltes &

Nesselroade, 1979). During the last decade, many methodologists have

added to the current knowledge base, and classic models for ‘‘growth-curve

analysis’’ seems to have been revived as an important research technique

(e.g., see Rogosa & Willett, 1985; McArdle & Epstein, 1987). In a general

sense, the term growth-curve analysis denotes the processes of describing,

testing hypotheses, and making scientific inferences about the growth and

change patterns in a wide range of time-related phenomena. More specif-

ically, growth curves are longitudinal data with special properties of repeated

measures. Thus, in both theory and practice, the term ‘‘growth curve’’ is not

limited to the phases of life, where the organism ‘‘grows,’’ but it can also be

used to describe and analyze phases of life, where the organism ‘‘declines.’’

The phenomena that are studied here both ‘‘grow and decline over age,’’ and

these comprehensive features lead to an unusual set of opportunities for

developmental analyses of data.

This paper describes a current application of one class of longitudinal

growth-curve analysis models – latent curve modeling (LCM) using struc-

tural equation modeling (SEM) techniques. The analytic techniques of this

chapter are presented in the next five sections. These are presented in a

sequence of difficulty, as ‘‘steps’’ of developmental data analysis. Step 1:

Describing the Observed and Unobserved Longitudinal Data. I consider some

Five Steps in Latent Curve Modeling with Longitudinal Life-Span Data 317

useful ways to summarize longitudinal data, including statistical informa-

tion from both the complete and incomplete cases. Step 2: Characterizing the

Developmental Shape of Both Individual and Groups. I try to describe both

the group and individual characteristics of the longitudinal data. We dem-

onstrate how the SEM approach is generally easy and flexible. Step 3: Ex-

amining the Predictors of Individual and Group Differences in Developmental

Shapes. We recognize that individual differences in growth may be the result

of combinations of other measured variables. I describe how SEM can be

used in a ‘‘multi-level’’ form, can be extended to include concepts from

latent path analysis, and can provide empirical evidence for hypotheses

about the precursors or correlates of individual longitudinal patterns. Step

4: Examining Group Differences in Developmental Shapes. Individual differ-

ences in growth may be the result of combinations of different patterns of

change for persons in different groups. To study these possibilities, I de-

scribe how SEM can be used in a ‘‘multi-group’’ form, can be extended to

include concepts from ‘‘latent mixture’’ models, and can provide empirical

evidence for hypotheses about the heterogeneity in longitudinal growth

patterns. Step 5: Studying Dynamic Determinants Among Variables Over

Time. I show how the time-dependent nature of the latent variables can be

represented in SEM and used to study ‘‘lead–lag’’ relations using simple

dynamic expressions.

As an illustration within all five steps, I present alternative analyses of

a longitudinal data collection on intellectual abilities over the life span

from the Bradway–McArdle Longitudinal Study (McArdle & Hamagami,

1996, 2004; McArdle, Hamagami, Meredith, & Bradway, 2001). In

Table 1, I outline the multiple phases of data collection on intellectual

abilities measured over a large part of the life span – at seven occasions

between ages 2 and 72. Some of the data collected appear in the plots of

Fig. 1 and will be described in the next section. Analyses of these life-

span data have been reported in more detail in other recent papers, so

here I mainly summarize these results and highlight the life-span features

of prior analyses. These illustrations are used to convey the main pre-

sumptions and techniques as well as the benefits and limitations involved

in using this approach in developmental research. Mathematical and sta-

tistical issues are not presented in detail, and formal equations are not

presented. This is an overview of the general data analysis methodology

to demonstrate the flexibility of these methods for life-span developmental

research.

JOHN J. MCARDLE318

Table 1. Overview of the Bradway–McArdle Longitudinal Data at

Seven Time Points (N ¼ 111).

Date Sample Size Age Range Measures Investigators

1931 212 2–7 SB RT+, QM

1941 138 12–17 SB+ KB

1956 111 27–32 SB, WAIS+ KB

1969 48 40–45 SB, WAIS+ JK+, KB

1984 54 55–60 WAIS, WJ-R+ JM+, KB

1992 51 61–66 WAIS, WJ-R+ JM+, KB

1998 32 67–72 WAIS, WJ-R+ JM+, KB

Notes:

1. Measurement batteries are the Stanford–Binet (SB), Wechsler Adult Intelligence Scale

(WAIS), and Woodcock–Johnson Revised (WJ-R). (+) indicates that additional demographic

and/or psychometric measurements were included.

2. Study Investigators were Robert L. Thorndike (RT), Quinn McNemar (QM), Katherine

P. Bradway (KB), Jon Kangas (JK), and John J. McArdle (JM).

Fig. 1. A Plot of Individual Growth Curves for the Bradway–McArdle Longitu-

dinal Data (N ¼ 111).


STEP 1: DESCRIBING THE OBSERVED AND

UNOBSERVED LONGITUDINAL DATA

The first step in any useful data analysis is an adequate description of the

data. The collection of longitudinal data can be extremely difficult, espe-

cially over large portions of the life-span, so the unique aspects of these data

should be emphasized.

The Bradway–McArdle Longitudinal Study

The data examined here come from persons, who were first measured in

1931 when they were aged 2–7 as part of the larger standardization sample

of the Stanford–Binet test (N ¼ 212). Many of these persons who could

be found (N ¼ 138) were measured again about 10 years later by Katherine

P. Bradway as part of her doctoral dissertation in 1944. Many of these same

persons were measured twice more by Bradway as adults at average ages of

30 and 42 using the Wechsler Adult Intelligence Scales (WAIS) (N ¼ 111;

for further details, see Bradway & Thompson, 1962; Kangas & Bradway,

1971). In 1984 and 1992, at average ages 57 and 65, the current author,

working with Bradway, measured as many of these same subjects as we

could locate (see McArdle & Hamagami, 1996; McArdle et al., 2001). About

half (N ¼ 55) of the adolescents tested in 1944 were measured again in 1984

at ages 55–57 and in 1992 at ages ranging from 64 to 72 (McArdle et al.,

2001). The seventh testing of the same individuals in 1998 yielded retests on

most of the same people (N ¼ 32), and attrition from the study was com-

plicated by the advancing age of the persons (e.g., attrition due to fatigue,

illness, and death). Although many other researchers were involved in data

collection (see Table 1), we now term this whole collection the Bradway–

McArdle Longitudinal study.

Up to now, our published analyses have focused only the first six time

points of longitudinal data. Fig. 1 is a display of individual growth curve

data for this measure at each age-at-testing for N ¼ 111 individuals. The

y-axis indexes the scores on ‘‘Verbal-Knowledge’’ and the x-axis is an index

of the age-at-testing. The connected lines in this figure are graphic descrip-

tions of the pattern of Verbal-Knowledge scores for each of the individuals,

so each line is termed a growth curves or trajectory. This kind of plot allows

us to see some overall trends of rapid rises in early childhood and adoles-

cence to long periods of consistency at older ages. There are also a hint of

JOHN J. MCARDLE320

patterns of growth and change for some individuals, and this interesting

possibility requires a rigorous examination.

The analysis of growth and change in similar constructs spanning child-

hood to adulthood still represents a major challenge for research over the

life span. It is easy to connect the data for a person over many time points of

testing, but it is much harder to insure that these scores meet the measure-

ment requirements of ‘‘exactly repeated measures.’’ Growth-curve analyses

typically require: (a) the same entities are repeatedly observed, (b) the same

procedures of measurement and scaling of observations are used, and (c) the

timing of the observations is known. In prior psychometric research, we

have used both scale-level factor analysis and Item Response Theory (IRT)

models to create comparable measurements over time. Composite scores

from the early Stanford–Binet tests (at ages of 4, 14, 30, and 42), as well as

the WAIS tests (at average ages 30, 42, 56, and 64) were used to form a

Verbal-Knowledge (or Crystallized Intelligence gc) score. These scores are in

a comparable metric, because they were based on a Rasch-scaling of selected

items in each test (see Hamagami, 1998; McArdle & Nesselroade, 2003;

McArdle et al., 2004).

Describing the Observed Data

The sample sizes, means, standard deviations, and correlations of these

IRT-scaled measures over six occasions are listed in Table 2. The overall

subject participation shows a nearly continual loss of participants over the

60 years. This is not an unusual loss of participants, even for a much smaller

period of time. The means and standard deviations show the simple pattern

described earlier, with rapid rises in childhood and adolescence together

with very little growth or decline in adulthood. The correlations over time,

the unique statistical information of the longitudinal data, present a com-

plex pattern of results, some correlations suggesting high stability of indi-

vidual differences (e.g., r40:9) and others suggesting low long-term stability

(ro0.1).

Fig. 2 is a scatter-plot matrix of the raw data at all occasions. In this

figure, the frequency histograms are placed on the main diagonal and the

pair-wise scatter plots are placed on the off-diagonals. These scatter plots of

growth data provide another way of describing the relative mobility of

cognition in these people over time. The spread of points is rather narrow

when the time interval is shortest (i.e., at consecutive measurements), and

the spread of points is much wider when the observations are most further


apart in time (i.e., from childhood at age 4 to adulthood at ages 42–65). The

correlation matrix is a simple summary of these features, but the plots are

uniquely informative about the patterns of the people.

Table 2. Observed and Unobserved Summary Statistics based on the

Longitudinal Data at Six Time Points (N ¼ 111;MLE-MAR in brackets;

Step 1, see Figs. 1 and 2).

(a) Observed means, standard deviations, and ranges

N Mean [MLE] SD [MLE] Min Max

Age 04 110 22.1 [22.2] 9.8 [9.6] 0.0 37.6

Age 14 111 72.2 [72.2] 9.3 [9.3] 40.6 90.2

Age 29 110 84.4 [84.4] 7.0 [6.9] 58.2 95.7

Age 42 49 87.9 [87.2] 6.7 [6.8] 69.2 100.0

Age 57 51 88.9 [87.7] 5.8 [7.0] 73.8 99.3

Age 65 51 88.1 [87.1] 7.1 [7.1] 68.7 98.6

(b)Observed and unobserved correlations (each entry includes pairwise r and [MLE-MAR r])

Age 04 Age 14 Age 29 Age 42 Age 57 Age 65

Age 04 1.000

Age 14 0.553 1.000

[0.558]

Age 29 0.233 0.680 1.000

[0.224] [0.679]

Age 42 0.194 0.377 0.812 1.000

[0.143] [0.412] [0.787]

Age 57 0.304 0.489 0.800 0.798 1.000

[0.211] [0.574] [0.858] [0.843]

Age 65 0.079 0.472 0.759 0.663 0.911 1.000

[0.171] [0.579] [0.791] [0.744] [0.946]

(c) Patterns of complete (x) and incomplete (o) data

1 2 3 4 5 6 7 8 9

Age 04 x x x x x x x x x

Age 14 x x x x x x x x x

Age 29 x x x x x x x x o

Age 42 o x o x o x o x x

Age 57 o x o o x x x o x

Age 65 o x x o o o x x x

Frequency 36 29 12 9 8 7 6 3 1

JOHN J. MCARDLE322

Results from Dealing with Incomplete Information

The summary information presented in Table 2 is not limited to only those

participants with complete data at all six time points of measurement. As is

usual, we can represent all available information about the observed means and

variances and the ‘‘pair-wise’’ correlations. To deal with this problem, I present

a description of the patterns of complete and incomplete data in Table 2c. In

these data, there are only nine different patterns of incomplete data (out of a

possible set of 70 patterns), and most of the persons are either measured at all

six times (n ¼ 29) or only the first three times (n ¼ 36). These incomplete data

patterns can be represented as the ‘‘percentage of data’’ or ‘‘coverage’’ for each

covariance of these scores – in some cases 100% of the participants are used but

in some cases only 29.7% of the information is available (at ages 42 and 65).

In Tables 2a and b, I also use brackets to list an ‘‘incomplete data’’

estimate of the sample means, standard deviations, and correlations. These

estimates are based on what is termed maximum likelihood estimation under

GC

_A

GE

04

GC

_A

GE

14

GC

_A

GE

29

GC

_A

GE

42

GC

_A

GE

57

GC_AGE04

GC

_A

GE

65

GC_AGE14 GC_AGE29 GC_AGE42 GC_AGE57 GC_AGE65

Fig. 2. Relationships among the Scores of the Six ‘‘General Knowledge’’ (Gc)

Variables Over Time.


missing at random assumptions (MLE-MAR; Little, 1995; McArdle, 1994;

Cnaan, Laird, & Slasor, 1997). This approach allows us to examine these

summary statistics ‘‘as if all persons were measured at all occasions.’’ These

newly estimated statistics are fairly close to the pair-wise estimates and this

indicates these data meet the minimal conditions of ‘‘missing at random’’

(MAR; Little, 1995). Most importantly, these estimated statistics do not

suffer from some common statistical problems (local linear dependency),

and we can generally use all available information from every person – that

is, we do not need to select a subset of persons with complete data (n ¼ 29)

out of all those measured (N ¼ 111).

Dealing with incomplete data as unobserved but important scores can alter

inferences about growth and change. For example, from Table 2b, the pair-

wise correlation of General Knowledge from age 4 to 42 is only r ¼ 0:19; andfrom age 4 to 65 only r ¼ 0:08; and these are certainly not as high as com-

parable correlations recently reported elsewhere (e.g., r47 in Deary, Whalley,

Lemmon, Crawford, & Starr, 2000). It would be reasonable to critique our

lower over-time correlations as being underestimated due to the loss of par-

ticipants (i.e., selective attrition). This concern leads us to try to account for

the patterns of incomplete data. It is interesting that when we do use con-

temporary technique to account for attrition, we obtain similar results – the

MLE-MAR-estimated correlation of General Knowledge from age 4 to 42 is

only r ¼ 0:14; and from age 4 to 65 only r ¼ 0:17 (from Table 2b). This new

result implies these low correlations over time may be due to initial selection but

are probably not attributable to subsequent attrition. This description of un-

observed statistics is an essential part of all contemporary growth models.

STEP 2: CHARACTERIZING DEVELOPMENTAL

SHAPES FOR GROUPS AND INDIVIDUALS

The second step in a useful data analysis is the attempt to highlight the key

features of the data in terms of a model. In contemporary behavioral science

research, one common approach to growth-curve analysis is to write a tra-

jectory equation for each group and individual. Some mathematical and

statistical aspects of these kinds of models are described next.

Linear Growth Models for Repeated Measures

Most forms of growth-curve analyses require longitudinal data with

repeated measurements where we observe the variable (Y) at multiple

JOHN J. MCARDLE324

occasions (t ¼ 1 to T) on the same person (n ¼ 1 to N) and we can symbolize

the scores (as Y ½t�n). This model includes three unobserved or latent scores

representing: (1) the individual’s initial level (y0), (2) the slopes representing

the individual linear change over time (y1), and (3) the independent errors of

measurements (e[t]). To indicate the form of the systematic change, we use a

set of group coefficients or basis weights, which define the timing or shape of

the trajectory over time (e.g., Age½t� ¼ t; or as A½t� ¼ AgeðtÞ). It is typical to

estimate the fixed group means for intercept and slopes (m0, m1) and also the

implied random variance and covariance terms (s02, s1

2, s01) describing the

distribution of individual deviations (d0n, d1n) around those means. We also

usually assume that there is only one random error variance (s2e), and the

error terms are assumed to be normally distributed and presumably uncor-

related with all other components.

The path diagram of Fig. 3 is an exact translation of the necessary matrix

algebra of these models. These diagrams can be conceptually useful devices

for understanding the basic modeling concepts. They are also practically

y0

d0

ys

ds

1

α [1]=0.40.4

α [2]=5.0

a[3]=6.2

a[4]=6.46.4α [5]=6.5

α [6]=6.5

σ0=9.89.8 σs=1.51.5

µs=10.710.7

0s� � = 0.77= 0.77

µ0=17.917.9

Y1

e1

Y2

e2

V3 Y4 V5 Y6

σe=17.417.4

Y5Y[1]

e[1]

Y[2]

e[2]

Y[4]

e[5]

Y[6]

e[6]

Y[3] Y[5]

17.417.4 17.417.4 17.417.4 17.417.4 17.417.4

e[3] e[4]

Fig. 3. A Path Diagram of Numerical Results from a Latent Basis Growth Model

from the Bradway–McArdle Longitudinal Data.


useful because they can be used to represent the input and output of any of

the SEM computer programs. These diagrams were originally used with

variations on multiple regression and factor analysis models, but contem-

porary work has shown how these diagrams can be used in the context of

growth and change (e.g., McArdle, 1986; McArdle & Epstein, 1987;

McArdle & Anderson, 1990; McArdle & Woodcock, 1997). In this path

diagram, the observed variables are drawn as squares, the unobserved var-

iables are drawn as circles, and the required constant is included as a tri-

angle. Model parameters representing ‘‘fixed’’ or ‘‘group’’ coefficients are

drawn as one-headed arrows while ‘‘random’’ or ‘‘individual’’ features are

drawn as two-headed arrows. In this case, the initial level and slopes are

often assumed to be random variables with ‘‘fixed’’ means (m0, m1) but

‘‘random’’ variances (s02, s1

2) and correlations (r01).

Basic Linear Growth Models

Some initial growth-curve modeling results for the Bradway–McArdle data

are presented in Table 3. In these longitudinal models, any change score (y1)

is assumed to be constant within an individual but it is not assumed to be the

same between individuals. We do not usually estimate the unobserved scores

but we do estimate several parameters, which characterize the key features

of these unobserved scores.

The first model labeled 3a is no-growth model fitted with only three pa-

rameters: An initial level mean (m0 ¼ 68:6), an initial standard deviation (s0o 0.01), and an error variance (s2e ¼ 740). The parameters also yields a

model likelihood (L2 ¼ �2276), which shows the no-growth baseline is a

poor fit compared to the totally unrestricted model ðw2 ¼ 1510; df ¼ 24).

This model is typically just used as a baseline against which to judge the fit

of more informative models.

The second linear growth model labeled 3b uses a fixed set of basis co-

efficients formed by taking A½t� ¼ ½Age½t�=10� or fixed values (e.g.,

A½t� ¼ ½0:4; 1:4; 2:9; 4:2; 5:7; 6:5�). This linear scaling is only one of many

that could be used, but it was chosen to permit a practical interpretation of

the slope parameters in terms of a per-decade change. In contrast to the no-

growth baseline, this linear growth model also has three more free param-

eters: a slope mean (m1), standard deviation (s1), and correlation (r01). This

model fitted yields a new likelihood (L2 ¼ �2092), which is a large distance

from the unrestricted model (w2 ¼ 1142 on df ¼ 3) but is an improvement

in fit over the previous baseline (M1 vs. M0: Dw2 ¼ 368 on Ddf ¼ 3).

JOHN J. MCARDLE326

The resulting means describe a function that starts low at age 4 (m0 ¼ 41:8)but increases between ages 4 and 65 (by m1 ¼ 9:5 per decade). The variance

estimates of the intercept and slope parameters are too small to interpret (sjo 0.02), but the error variance has been reduced (from s2efM0g ¼ 740 to

s2efM1g ¼ 345). Of course, the main problem is that simple straight-line

linear growth model does not fit these data very well.

Table 3. Selected Results from Five Latent Growth Models Fitted to

Longitudinal Data at Six Time Points (N ¼ 111; Step 2, see Figs. 3–5).

Parameters 3a (Level) 3b (Linear) 3c (Quadratic) 3d (Latent) 3e (Spline)

Fixed effects

Basis a[04] 1, 0 1, 0.4 1, 0.4, 120.42 1, 0.4 1, 0, 0

Basis a[14] 1, 0 1, 1.4 1, 1.4, 121.42 1, 5.0 (0.6) 1, 1, 0

Basis a[29] 1, 0 1, 2.9 1, 2.9, 122.92 1, 6.2 (0.07) 1, 1, 0.80

(0.03)

Basis a[42] 1, 0 1, 4.2 1, 4.2, 124.22 1, 6.4 (0.08) 1, 1, 0.98

(0.04)

Basis a[57] 1, 0 1, 5.7 1, 5.7, 125.72 1, 6.5 (0.08) 1, 1, 1.01

(0.04)

Basis a[65] 1, 0 1, 6.5 1, 6.5, 126.52 1, 6.5 1, 1, 1

Level m0 68.6 (1.2) 41.8 (1.4) 47.8 (?) 17.9 (1.0) 22.2 (0.94)

Slope m1 — 9.5 (0.40) 0.42 (?) 10.7 (0.19) 50.0 (0.86)

Accelerate ma — — 0.12 (?) — 15.1 (0.86)

Random effects

Error se2 740 (54) 345 (28) 41.3 (?) 17.4 (1.5) 8.3 (0.95)

Level s0 o0.01 (?) o0.01 (?) 22.3 (?) 9.8 (0.79) 9.4 (0.69)

Slope s1 — o0.01 (?) 29.3 (?) 1.5 (0.14) 8.1 (0.69)

Correlation

r01

— 40.99 (?) �0.77 (?) �0.77 (0.05) �0.50 (0.08)

Accelerate sa — — 15.9 (?) — 7.1 (0.66)

Correlation

r0a, rsa

— — 0.33 (?), �0.84

(?)

— �0.60 (0.08),

�0.04 (0.12)

Fit indices

Numbers of

parameters

3 6 10 10 13

Degree of

freedom

24 21 17 17 14

Log likelihood �2276 �2092 �1830 �1583 �1507

Likelihood

ratio (DLL)

1510 41142 4617 122 46

RMSEA 0.75 0.69 0.56 0.23 0.14

Note: ‘?’ freely estimated from data.


Including Polynomial Nonlinearity in Growth

The data of Fig. 1 suggest some nonlinearity over age in the scores of almost

all persons. To deal with this complexity, Wishart (1938) introduced the use

of power polynomials to better fit the curvature apparent in growth data.

The individual growth curve (consisting of t ¼ 1; T occasions) is summa-

rized into a small set of linear orthogonal polynomial coefficients based on a

fixed power-series of time (A[t], A[t]2, A[t]3,y, A[t]p) describing the general

nonlinear shape of the growth curve. A second-order (quadratic) polyno-

mial growth model can be introduced using a new set of component scores

(e.g., yp) introduced to represent another level of change with a set of pow-

ered coefficients (1=pA½t�p) . This leads to an implied change model, where

the change is linear with time (i.e., acceleration). Of course, a model of

growth data might require this form of a second-order (quadratic), third-

order (cubic), or even higher-order polynomial model fitted to the data. In

all cases, additional variance and covariance terms are added to account for

individual differences in latent scores. This polynomial growth curve ap-

proach remains popular (e.g., Bryk & Raudenbush, 1992).

A quadratic polynomial model 3c was fitted next, including an additional

slope variable (acceleration) defined by a fixed basis ð12A½t�2Þ; one new mean,

one new deviation, and two additional correlations. This 10 parameter

model is still far worse than the unrestricted model (w24617 on df ¼ 17),

but is an improved fit compared to the linear model (M2 vs. M1: Dw2 ¼ 524

on Ddf ¼ 3). The error or unique variance has also been reduced substan-

tially (from s2efM2g ¼ 41:3) but problems arose in the variance–covariance

estimation (sj o 0.02), numerical convergence was not achieved, and this

flexible polynomial model seems to inappropriate for these data.

Nonlinearity Using Latent Basis Curves

A different alternative of the linear growth model was proposed by Rao

(1958) and Tucker (1958, 1966) in the form of summations of ‘‘latent

curves’’ (see Meredith & Tisak, 1990). The use of this latent growth curve

offers a relatively simple way to investigate the shape of a growth curve – we

allow the curve basis to take on a form based on the empirical data. In this

approach, we write a model for the same person at different occasions but

some of the basis coefficients (e.g., a[2], a[3], a[4], and a[5]) are free to be

estimated. That is, the actual ages of the persons are known but the basis

parameters are allowed to be freely estimated, and we end up with an op-

timal shape for the group curve and individual differences. In this model, the

JOHN J. MCARDLE328

A[t] is estimated just like a common factor loading and thus has mathe-

matical and statistical identification problems. There are many alternative

ways to estimate these parameters but, in general, there are many free pa-

rameters in the two-component growth model (p ¼ 6þ T � 1). The use of

an estimated basis has been termed a ‘‘meta-meter’’ or ‘‘latent time’’ scale

that can be plotted against the actual age curve for visual interpretation (see

McArdle & Epstein, 1987).

The fourth model fitted 3d was a latent basis growth model where some of

the loadings, A[t], are free to vary. For the purposes of estimation, we fixed

A½1� ¼ 0:4 (at age ¼ 4) and A½65� ¼ 6:5 (at age ¼ 65), but the four other

coefficients were estimated from the data. This results in a large improvement

in the model likelihood (L2 ¼ �1583), which is much closer to the unrestricted

model (w2 ¼ 122 on df ¼ 17), and substantially better than the nested baseline

model (Dw2 ¼ 1386 on Ddf ¼ 7), the nested linear model (Dw2 ¼ 1018 on

Ddf ¼ 4), and better than the non-nested quadratic model with the same

numbers of parameters (�1830 vs. �1583). The error variance has also been

reduced (s2efM3g ¼ 17:4). The interpretation of the model parameters is an

important part of the latent growth analysis, and these are displayed in Figs. 3

and 4. The estimated latent means are m0 ¼ 17:9 and m1 ¼ 10:7; their devi-

ations are s0 ¼ 9:8 and s1 ¼ 1:5; and the two latent factors have correlation

of r01 ¼ �0:77: These values were included on the path diagram of Fig. 3. The

estimated basis coefficients were A½t� ¼ ½0:4; 5:0; 6:2; 6:4; 6:5; 6:5�; and these

are repeated in Fig. 4 also (and discussed later).

Nonlinearity Using Linear Spline Models

Another way to deal with nonlinearity is by introducing the concepts of

connected lines or splines (Seber & Wild, 1989). As a simple example here, we

could write a model for the same person at different occasions where we

define some critical age (i.e., C ¼ 30 years), and estimate two slope scores –

y1 before the age C and y2 after age C. This implies the estimated parameters

for the means and covariances of the intercept are now ‘‘re-centered’’ at age

C, and the slopes represent changes before and after age C. These models

can be combined with the polynomial models, and the critical cutoff ages

(Cn) can be estimated as well and these concepts can create a potentially

informative look at individual segments of growth (e.g., Cudeck & du Toit,

2001; Hamagami & McArdle, 2001).

This leads us naturally to the idea for the final model 3e, which is a spline

model composed of two latent slopes – the first representing changes up

to age 14 and the second representing changes after age 14. The exact


Solid line = Exponential growth

model

(no decline in later ages)

Dashed line = Latent Basis model

(allowing decline at any age)

Fig. 4. Latent Growth Score Expectations for the General Knowledge Scores. (a) Group Expectations from Two Models.

(b) Individual Expectations from the Final Model.

JOHN

J.M

CARDLE

330

parameterization we used yielded a starting level of 22.2 at age 4 with a big

average gain jump of 50 points (79.4) up to age 14, followed by a small

steady gain of 15.1 points (78.1) from 14 to 65. However, this two latent

splines should not be ignored, because it shows much better fit overall

(w2 ¼ 46 on df ¼ 14) and when compared to the nested one-slope model

(M6 vs. M4: Dw2 ¼ 76 on Ddf ¼ 3) is notable.

Model-Based Expectations about Growth

In the models just presented, the changes within a person is initially rep-

resented by the latent means and variance terms in the growth models. In

later interpretations, we can examine the relative size of these parameters

and make substantive interpretations about the group and individual dif-

ferences. These parameters also allow us to form the expected growth curve

charts for both the observed and true scores. Details of derivations are not

presented here, but it is still useful to consider some key properties of the

model (see McArdle, 1986, 1989; McArdle & Woodcock, 1997). These pa-

rameters can be combined to create expected means (my½t� ¼ m0 þ m1A½t�).

Here, we obtain the expected group curve at mean scores of m½t� ¼

½22:2; 72:1; 84:4; 86:6; 87:6; 87:7�: These are plotted in Fig. 5a. This is a

growth curve with a shape that rises quickly between ages 4 and 14, peaks at

age 42, and exhibits no declines by age 65. All coefficients can be interpreted

in terms of changes over decades, so the average 10-year change is 10.7

points, but a person who is 14 years old can be said to have the latent age of

a 50 year old (i.e., A½14� ¼ 5:0). The individual differences in this model are

seen in the variances for the level (s20 ¼ 9:52) and the slope (s21 ¼ 1:52) pa-rameters. If we consider 100% of the changes between ages 4 and 65 (the

observed period), we now say that 76% of all cognitive growth in knowledge

occurred by age 14, 95% by age 42, and so on. The highest estimated

coefficient never was higher than the fixed value at age 65 – this is important

because it means the Verbal-Knowledge ability does not decline within

people repeatedly measured over this span of ages. This is not the typical

finding with cross-sectional age comparisons (e.g., McArdle et al., 2002).

Goodness-of-Fit, Expectations and Residuals Over Time

The choice between using a latent basis, or a polynomial, or spline or other

nonlinear model can be substantively important. The initial idea from

Wishart (1938) was that the basic shape of each individual curve could be

captured with a small number of fixed parameters and random variance


Fig. 5. Individual Level Latent Growth Score Parameters and Residuals. (a) Individual Estimates of Latent Levels and

Slopes. (b) Individual Residuals of the Final Growth Model.

JOHN

J.M

CARDLE

332

components. In some cases, a fixed basis polynomial model is more par-

simonious than a free basis model. However, the polynomial model also has:

(a) a relatively fixed curvature and (b) requires an additional estimation of

covariances among the new latent scores (yp; this is true even if orthogonal

polynomial coefficients are used). Thus, the polynomial model may add

more complexity (via parameters to be estimated) than is actually needed

and the latent basis model may prove to be more efficient and interpretable.

Although many recent textbooks overlook the latent basis model (e.g.,

Duncan, T. E., Duncan, S. C., Strycker, Li, & Alpert, 1998; Singer &

Willett, 2003), we always treat this as an empirical choice (McArdle & Bell,

2000; McArdle & Nesselroade, 2003).

We do not usually need to make any specific decision about these model

differences at this point. It is enough to say that the two-part linear slope

model fits best, it is far better than the others considered, and it is far more

interesting than simply adding unique variances or covariances to improve

fit. However, the interpretation of the two-slope model is very similar to

what we have already described in the one-slope model, which changes

direction. The one latent slope model may need to be reconsidered and the

complexity of individual differences should not be overlooked.

There are many other models that could fit to these data, and others have

done so (e.g., Cudeck & du Toit, 2001; Hamagami & McArdle, 2001). In

such an enterprise it may be important to do a more detailed analysis of the

curve at the individual level. In Fig. 5a, we plot individual estimates of the

latent parameters for the level and slope (These are Bayes-restricted esti-

mates based on the latent model 3d). In this plot, many of the individuals

have estimated levels and slopes in large cluster, but other individuals are

scattered with negative slopes. The variation apparent here will be examined

in the next section. The second plot 5b is a description of the residuals

between the individual expected curve (based on the estimated level and

slope) and the observed data. Here it appears that at least one individual is

poorly represented (i.e., the large positive residual at age 45), and there is

much more misfit at age 14 than at any other point in time. These are aspects

of model fitting that need to be improved with subsequent analyses.

STEP 3: MODELING PREDICTORS OF INDIVIDUAL

DIFFERENCES IN DEVELOPMENTAL SCORES

The group mean parameters estimated in the previous analyses allow us

to plot the group trajectory over time. Similarly the estimated variance


parameters allow us to consider the size of the between group differences at

each age. However, no prior information obtained in model fitting tells us

about the sources of this common variance. To further explore the differences

between persons we need to expand the basic latent growth model to include

impacts on the latent parameters.

The Multilevel Growth Model

Let us assume a variable termed X indicates some measurable difference

between persons (e.g., sex, educational level, etc.). If we measure this var-

iable at one occasion, we might like to examine its influence in the context of

a growth model for Y[t]. One popular model is based on the use of ‘‘ad-

justed’’ growth parameters as popularly represented in the techniques of the

analysis of covariance. In growth curve terms this model is written with fixed

(group) coefficients (g) with some effect on the measured Y[t] scores at each

occasions, and the X is an independent observed (or assigned) predictor

variable. In this case, the growth parameters (m0:x;m1:x; s0:x;s1:x;s0;1:x) areconditional on the expected values of the measured X variable. The pa-

rameters of the changes may be defined by the resulting difference or dif-

ferential equation, but the reduction of error variance (sex) is often

considered as a way to understand the overall impacts.

The apparent complexity of the covariance model leads to a simpler and

increasingly popular way to add an external variable – we can write a mixed-

or multi-level model, where the X variable has a direct effect on the param-

eters of the growth curve. This means we have intercepts (v) and regression

slopes (g) for the effect of X on the two latent components of Y[t]. This

model is drawn as a path diagram in Fig. 6. This diagram is the same as

Fig. 3 except that here, we have included several X variables as predictors of

the levels and slope components. It may be useful to write this as a reduced

form of SEM, so it is now clear that the three unobserved residual terms are

not simply separable by standard linear regression or covariance equation

(see McArdle & Hamagami, 1996).

This path diagram gives the basic idea of external variable models, several

other more complex alternatives will be considered in later sections. In this

simple linear model, as in more complex models to follow, we can always add

predictors X for the intercepts and the slopes. In some areas of research these

models have been termed mixed-effects models (Laird & Ware, 1982; Littell,

Miliken, Stoup, & Wolfinger, 1996; Singer, 1999). In other areas of research

these same models have been termed random-coefficients or multi-level

JOHN J. MCARDLE334

models, or slopes as outcomes or hierarchical linear models (e.g., Bryk &

Raudenbush, 1992). In other SEM research these models were considered

using factor analysis terminology as latent growth models with incomplete

data and extension variables (e.g., McArdle & Epstein, 1987; McArdle &

Hamagami, 1992). Using any terminology, these models can be generically

represented by the parameters in the path diagram of Fig. 6, and this is a

common way to understand the between group differences in within group

changes. Once considered in this way, no new model fitting procedure is

required.

Results for Educational Influences on Latent Scores

A variety of additional variables have been measured in the Bradway–

McArdle collections, including demographic (e.g., gender, educational at-

tainment by age 30 and 56, etc.), self reported health behaviors (e.g., smok-

ing, drinking, physical exercise, etc.) and other problems (e.g., general

Y1

e1

Y2

e2

V3

e3

Y4

e4

V5

e5

y0

e0*

ys

es*

Y6

e6

1

=0=0.76.76

.95.95.98.98 1.01.0 =1=1

9.39.3 8.98.963.263.2

2.12.1

−.53−.53

.02.02 1.341.34

Educ

Person

21.921.9

Y3Y5

Y[1]

e[1]

Y[2]

e[2]

V3

e[3]

Y[4]

e[4]

V5

e[5]

Y[6]

e[6]

Y[3] Y[5]

17.417.4 17.417.4 17.417.4

Educ

Dad

Educ

Mom.08.08 .25.25

8.82

17.417.417.417.417.417.4

Fig. 6. Results from Latent Growth With Mixed-effects or Multi-level Predictors

Latent Path Predictors.


health, illness, medical procedures, etc.), and personality measures (e.g., 16

PF factors). In the analyses presented here we consider four variables: (1)

Gender (effect coded as –0.5 for males and +0.5 for females), (2) the per-

son’s own educational level (at age 30 in years), (3) Father’s educational

level (in years), and (4) Mother’s educational level (in years).

We started with a baseline latent growth model with no predictors of the

levels and slopes but then added additional variables as predictors of these

levels and slopes (as in Fig. 6). In these mixed-models, we only fit the latent

basis curve model (with a 0–1 basis), and we added Gender as an effect-

coded variable (i.e., M ¼ �0:5 and F ¼ þ0:5), and the three indices of

educational attainment centered at 12 years.

Table 4 is a list of results including these four variables and the six in-

tellectual ability measures. The first model 4a used all four variables and has

a misfit (w2 ¼ 153 on df ¼ 33), which is a reasonable fit when compared to

Table 4. Selected Results from Two Latent Growth Models with Four

Extension Variables Fit to the Longitudinal Data at Six Occasions (Step

3, see Fig. 7).

Parameters 4a: {Latent} Level 4a: {Latent} Slope 4b: {Latent} Level 4b: {Latent} Slope

Fixed effects

Basis A[t] [ ¼ 0, 0.76, 0.95, 0.98, 1.00, ¼ 1] [ ¼ 0, 0.76, 0.95, 0.98, 1.00, ¼ 1]

Intercept g0 22.8 (1.5) [0] 62.6 (1.7) [0] 23.3 (1.5) [0] 62.6 (1.6) [0]

Regression from

Gender gg

0.73 (2.0) [0.04] 1.63 (2.0) [0.09] 0 0

Regression from

Educ_Pers gp

0.20 (0.04) [0.048] 1.32 (0.47) [0.32] �0.13 (0.44) [0.00] 1.44 (0.45) [0.35]

Regression from

Educ_Dad gd

0.95 (0.47) [0.32] �0.68 (0.48)

[�0.23]

0.58 (0.38) [0.19] �0.24 (0.39)

[�0.08]

Regression from

Educ_Mom gm

�0.78 (0.54)

[�0.23]

0.65 (0.55) [0.19] 0 0

Random effects

Residual sd 9.0 (0.75) 8.7 (0.82) 9.1 (0.75) 8.8 (0.82)

Error se2 17.4 (1.5) 17.5 (1.5)

Correlation rd0, ds �0.81 (0.04) �0.81 (0.04)

Fit indices

Numbers of

parameters

32 28

Degree of freedom 33 37

Likelihood ratio 153 159

RMSEA 0.18 0.17

Note: Latent growth with all four predictors set to zero yields L2 ¼ 195 on df ¼ 41.

JOHN J. MCARDLE336

free latent curve model (M7 vs. M4: Dw2 ¼ 32 on Ddf ¼ 16). This implies

that the patterning of the correlations of predictors and outcomes can be

estimated using two latent variables (see McArdle & Prescott, 1992). The

MLE parameters suggest the following interpretations: 0 – The latent basis

coefficients (A[t]) were unaffected by the inclusion of the predictors.

1 – There are no accurate (significant) differences between Males and

Females on either levels or slopes. 2 – The Person’s Educational level (at age

30) was not predictive of the initial cognitive level (at age 4) but was a

positive indicator of overall changes in adulthood (g ¼ 1:32½0:32�). 3 – The

educational level of the Father (at age 14) was a positive contributor to the

initial cognitive level (g ¼ 0:95½0:47�) but not to the adult slope. 4 – The

educational level of the Mother (at age 14) was not related to the initial level

or slope.

The second set of results 4b was based on a model, where all coefficients

for the Gender and Mothers’ Education were fixed at zero. This model fits

the data just about as well as before (M8 vs. M7: Dw2 ¼ 6 on Ddf ¼ 4), but

the results obtained now differs in one respect – the coefficient for the

Father’s Education on initial level is no longer accurate. This is probably

due to the fact that the Mother’s Education (a) no longer has a direct effect,

(b) it is positively correlated (r ¼ 0:62) with Father’s Education, and (c) the

previous ‘‘suppression’’ effect has been eliminated and the coefficient is not

as strong. In essence, the previous interpretation (4a) needed to consider the

negative coefficient of Mother’s Education, and reinterpret the overall im-

pact or do so in terms of the difference between parental educational levels.

Upto this point, it seems the only direct educational influence found here

is that of the Person’s Educational level on their own slope. However, the

model can be expanded to include more complex path representations, and

this leads to a comprehensive series of mixed-model path equations. As-

suming the three education variables are centered at grade 12, and we view

the person’s educational attainment as an outcome of the parent’s educa-

tional attainment, we estimated a simultaneous latent path model where the

Mother’s Education (0.25) and Father’s Education (0.08) had direct effects

on the Person’s Education, but only the Person’s Education had an effect on

the latent level (0.20) and slope (1.34). This resulted in a good overall fit

(w2 ¼ 162 on df ¼ 39; so Dw2 ¼ 3 on Ddf ¼ 2), and emphasizes the positive

but indirect effects of the Mother (0.025� 1.34) on the adult slopes of their

children. This model is drawn as an SEM path diagram in Fig. 6 to illustrate

how the basic principles of multiple regression and path analysis follow

directly to the latent variables of a growth model (see McArdle, 2001;

McArdle & Hamagami, 2003).


STEP 4: STUDIES OF GROUP DIFFERENCES IN

DEVELOPMENTAL SHAPES

The next step in longitudinal data analyses deals with the investigation of

group differences in developmental shapes or trajectories. While this is a

natural question raised in any multiple group SEM context, it is often

overlooked in this literature on mixed-effects or multi-level models. This

contrast between the two approaches was an issue raised by McArdle and

Epstein (1987) and McArdle (1990), and we now explore some alternatives.

Group Differences from a Multiple Group Perspective

One initial representation of group differences uses a new set of estimated

parameters to summarize between groups. This idea is clearly represented by

coding a set of variables (X) to characterizing the group differences and

then examining the effect of this set (X) on the model parameters (see, e.g.,

Fig. 6). However, this method is limiting in a number of important ways.

For example, some reasonable forms of growth processes are not immedi-

ately possible to account for within the standard framework. For example,

different groups of people could have different ‘‘amplitude’’ or be in dif-

ferent ‘‘phases’’ in their characteristic growth pattern. These features of

growth are not separated within the basic level and shape parameters al-

though these features may be realistic features of development.

An SEM treatment of this kind of a model uses concepts derived from

multiple-group factor analysis (e.g., Joreskog & Sorbom, 1979; McArdle &

Cattell, 1994). In these kinds of models, each group (g ¼ 1toG) is assumed to

follow a latent growth model where the basis parameters (A½t�ðgÞ) are definedby the application. Fig. 7 is a path diagram representing these kinds of

multiple group growth models (from McArdle & Hamagami, 1992). Since

the groups need to be independent (each person can only be in one group),

this kind of grouping is most easily done for discrete categorical variables

(i.e., sex, but not educational level).

In a most fundamental form, the multiple group growth model permits

the examination of the presumed invariance of the latent basis functions

(i.e., A[t](1) ¼ A[t](2) ¼ y ¼ A[t](g) ¼ y ¼ A[t](G)). The rejection of this

model implies that each independent group has a different shape of growth.

This parametric model is not one that is easily represented using standard

mixed-effects or multi-level models (for details, see McArdle & Hamagami,

1996). If invariance is found, we can also examine the equality of the

JOHN J. MCARDLE338

σσe

Y[1]

e[1]

Y[2]

e[2]

V

e[3]

Y[4]

e[4] e[5]

y0

y0*

ys

ys*

Y[6]

e[6]

1

α1

α2

α3 α4 α5α6

σ0 σsµs

0,s

µ0

Y[3] Y[5]

σe σe σe σe σe

Group

ρ ρ

1

σe

Y[1]

e[1]

Y[2]

e[2]

V

e[3]

Y[4]

e[4] e[5]

y0

y0*

ys

ys*

Y[6]

e[6]

1

α1

α2

α3 α4 α5α6

σ0 σsµs

0,s

µ0

Y[3] Y[5]

σe σe σe σe σe

Group

2

Fig. 7. A Latent Growth Curve Path Model for Separate Groups.

Five

Step

sin

Laten

tCurve

Modelin

gwith

Longitu

dinalLife-S

panData

339

variances of the latent levels and slope (sðgÞ0 ¼ . . . s

ðGÞ0 and sðgÞs ¼ . . . sðGÞs ).

Further analyses could include the error deviations (sðgÞe ), the total slope

variance and covariances, and functions of all the other parameters. We may

bring back the typical mixed-effects group difference parameters when we

examine the invariance of the latent means for initial levels and slopes

(mðgÞ0 ¼ m

ðGÞ0 and m

ðgÞ1 ¼ m

ðGÞ1 ). Of course, these group differences in the fixed

effects can be coded in the same way as in the typical mixed-effects analyses.

However, in important ways, each of these multiple group hypotheses rep-

resents a different kind of nonlinearity than was possible to examine using

the mixed-effects approach.

Results for Group Differences in Cognitive Growth for Males and Females

The group differences due to sex, education, and complete versus incomplete

data were also studied using the multiple group growth curves approach. To

illustrate this kind of analysis here, Table 5 gives the initial longitudinal data

on the six intellectual ability measures separately for each Gender. In these

cases, the two groups are created so the unrestricted likelihood for these

data is based on two sets of mean and covariance matrices; one for males

and one for females.

The first model (5a) allows both groups to have completely different

latent growth curves. The model now includes 10 parameters for each group,

and the 20 MLEs are listed in the first two columns. This results in a fairly

reasonable fit to both data sets (w2 ¼ 148 on df ¼ 34). A few small differ-

ences in MLEs can be seen between the two groups, but the key difference

appears to be the smaller error variance for the females (Ffs2eg ¼ 13:9 but

Mfs2eg ¼ 21:0). The second model (5b) adds the restriction that the latent

basis coefficients, while free to vary, must be identical across males and

females. This model is similar in fit to the free model (w2 ¼ 152 on df ¼ 38;

Dw2 ¼ 4 on Ddf ¼ 4), and this indicates the shapes of the curves may be

considered to be parallel across both groups.

The third model (5c) adds the restriction that all latent basis parameters,

while free to vary, must be identical across males and females. This model is

similar in fit to the free model (w2 ¼ 169 on df ¼ 44) and various compar-

isons are clear. (a) This is slightly worse in fit than the previous parallel

shape model (w2 ¼ 17 on Ddf ¼ 6), indicating some of the latent means or

covariances are different – and it is the error variance here. (b) This is not

much worse in fit than the free group model (w2 ¼ 21 on Ddf ¼ 10), indi-

cating not much overall differences between groups. (c) This is a bit worse in

JOHN J. MCARDLE340

fit than the one-group model (w2 ¼ 48 on Ddf ¼ 10), even though the MLE

parameters are identical, and this indicates there may be some basic dif-

ferences between groups that are not captured by this growth model.

As suggested earlier, these multiple group growth models have been used

to compare the complete and incomplete subsets of these data. In all such

cases, the key results for (a) the complete data only and for (b) the complete

and incomplete data together, and the parameters are much the same (as in

Diggle & Kenward, 1994; Little, 1995; McArdle, 1994). As a statistical test

for parameter invariance over these groups we calculated the difference in

Table 5. Numerical Results from Multiple Group Latent Growth

Models Fitted to Male & Female Longitudinal Data (Step 4, see Fig. 8).

Growth Model

Parameters

5a: Latent

Growth for

Males (n ¼ 52)

5a: Latent

Growth for

Females (n ¼ 59)

5b: Loading

Invariance over

Both Groups

5c: Total

Invariance over

Both Groups

Fixed effects

Basis a[04] 1, 0 1, 0 1, 0 1, 0

Basis a[14] 1, 0.75 (0.02) 1, 0.77 (0.01) 1, 0.76 (0.01) 1, 0.76 (0.01)

Basis a[29] 1, 0.94 (0.02) 1, 0.96 (0.01) 1, 0.95 (0.01) 1, 0.95 (0.01)

Basis a[42] 1, 0.99 (0.02) 1, 0.98 (0.02) 1, 0.98 (0.01) 1, 0.98 (0.01)

Basis a[57] 1, 1.01 (0.02) 1, 1.00 (0.02) 1, 1.00 (0.01) 1, 1.00 (0.01)

Basis a[65] 1, 1 1, 1 1, 1 1, 1

Level m0 21.6 (1.4) 22.7 (1.4) 21.5(1.4), 22.8

(1.4)

22.2 (0.97)

Slope ms 65.0 (1.9) 65.9 (1.5) 64.8 (1.6), 66.0

(1.5)

65.5 (1.2)vsp:0.5

Random effects

Error se2 21.0 (2.7) 13.9 (1.6) 21.4 (2.8), 14.0

(1.7)

17.4 (1.5)

Level s0 8.7 (1.1) 9.8 (1.0) 8.7 (1.1),9.7 (1.0) 9.3 (0.75)

Slope ss 9.7 (1.3) 9.3 (1.1) 9.6 (1.2), 9.2

(1.1)

9.4 (0.84)

Correlation r0s �0.64 (0.05) �0.85 (0.05) �0.63

(0.11),�0.85

(0.05)

�0.74 (0.05)

Numbers of

parameters

20 16 10

Log likelihood �1571. �1573 �1582

Likelihood ratio 148 152 169

Degree of

freedom

34 38 44

RMSEA 0.24 0.23 0.23


the likelihoods, and these differences are trivial (w2 ¼ 21 on Ddf ¼ 10). This

suggests that selective dropout or subject attrition can be considered ran-

dom with respect to the General Knowledge abilities measured here. This

result allowed us to combine the complete and incomplete data for a more

powerful analysis.

Mixture Models for Latent Groups

Another fundamental problem is the discrimination of models of multiple

curves for the same people from models of multiple groups with different

curves. It is possible for us to have, say, three clusters of people each with a

distinct growth curve but when we aggregate information over all the people

we end up with three multiple factors and mixed curves. This is the essence

of a latent grouping of people, and parallels the focus on a ‘‘person centered

approach’’ to multivariate data analysis (e.g., Block, 1971; Cattell & Birkett,

1980; Magnusson, 1995).

The recent series of models termed growth mixture models have been

developed for this purpose (Wedel & DeSarbo, 2002; Muthen & Muthen,

1995, 2002; Nagin, 1999). In these analyses, the distribution of the latent

parameters are assumed to come from a ‘‘mixture’’ of two or more over-

lapping distributions. Current techniques in mixture models have largely

been developed under the assumption of a small number of discrete or

probabilistic ‘‘classes of persons’’ based on mixtures of multivariate nor-

mals (e.g., two classes). More formally, we can write a model as a prob-

ability weighted sum of curves, where the probability of class membership

(Prob{cn}) is defined for the person in c ¼ 1 to C classes. In this kind of

growth mixture analysis we estimate the most likely threshold parameter

for each latent distribution (tp, for the pth parameter), while simultane-

ously estimate the separate model parameters for the resulting latent

groups.

The growth mixture models may be seen as a model-restricted fuzzy-set

cluster analysis – a multiple group model without exact knowledge of group

membership for each individual. The concept of an unknown or latent

grouping can be successively based on the logic of multiple group factorial

invariance – starting with equality of latent level means and variances, then

on the latent slope means and variances, then on both the level and slope,

then on the growth loadings, and so on. The resulting MLEs yield a like-

lihood which can be compared to the results obtained from a model with

one less class, so the mixture model distribution can be treated as a hy-

pothesis to be investigated. As in standard discriminant analysis, we can also

JOHN J. MCARDLE342

estimate the probability of assignment of individuals to each class in the

mixture. A variety of new computer programs have been developed for this

purpose (e.g., Mplus, by Muthen & Muthen, 2000).

Results from Latent Mixture Models

These latent growth mixture models were fit using all the longitudinal data

(Fig. 1) and some results are described in Fig. 8. In a first latent mixture

model, we estimated a two-class model with free parameters for both

groups. This model required 21 parameters, adding the one more than the

previous two-group model, and led to another likelihood (L2 ¼ �1544). We

need to recognize that the statistical basis of this comparison is still some-

what controversial, but if we consider the threshold as an implied parameter

in some previous models, we can get some sense of the gain in fit. The extra

parameter fitted was the threshold (t ¼ �0:55) – a z-score that suggests the

total group can be considered a mixture of two classes of different sizes,

n ¼ 72 and 39, with different growth patterns. By contrast to the one-class

model (w2 ¼ 78 on df ¼ 10) or even versus the unrestricted two-group model

(w2 ¼ 92 on df ¼ 1), two groups are better than one.

By inspection, we can see that the larger group has a growth pattern with

parameters that are very similar to the overall pattern described earlier (i.e.,

0

10

20

30

40

50

60

70

80

90

100

0 4 14 30 42 56 65 75

Model 1, Class 1

Model 1, Class 2

Age (Years)

0

10

20

30

40

50

60

70

80

90

100

0 4 14 30 42 56 65 75

Model 2, Class 1

Model 2, Class 2

Age (Years)

Same latent basis A[t],

Fit χ2=30, df=3,

Prob(Class1)=.92

Free latent basis A[t],

Fit χ2=34, df=12,

Prob(Class1)=.67

Fig. 8. Results from SEM Growth-Mixture Model with Two ‘‘Classes’’ of Growth

Curves. (Note: Model Includes Two Classes of LGMs and Probability of Group

Assignment (Results from Mplus).)


M4). However, the smaller second group seems to have a higher initial basis

(a½t�ð1Þ ¼ 0:73 vs. a½t�ð2Þ ¼ 0:84), a much higher initial mean (mð1Þ0 ¼ 16:9 vs.

mð2Þ0 ¼ 31:4), lower slope changes (mð1Þ1 ¼ 71:1 vs. m

ð2Þ1 ¼ 55:7), and much low-

er variances (sð1Þ0 ¼ 7:1 vs. s

ð2Þ0 ¼ 1:9). This second group shows almost no

variance in slope (sð1Þ1 ¼ 7:1 vs. sð2Þ1 40:1), and this could reflect an estimation

problem, or even a problem of limitation of measurement. Assuming these

are not critical problems, the second smaller class appears to be an initially

higher functioning group which slowly moves toward the final adult levels.

In second latent mixture model, we allowed the possibility of two latent

classes (C ¼ 2) with different parameters for the latent means and variance

but assuming the same growth basis. This results in a more equal separation

of persons (t ¼ �0:07; so n ¼ 56 and 55) with same free basis coefficients

(A½t� ¼ ½¼ 0; 0:76; 0:95; 0:98; 1:00;¼ 1�), but with the same pattern of means

and variances as the first model. This model loses a bit in fit (L2 ¼ �1565; soM14 vs. M13: Dw2 ¼ 36 on Ddf ¼ 6), so it may not be a good idea to force

exactly the same shape on groups that start at different points. Either way,

this still implies that a small group of persons started much higher than

average score and had a smaller change over time but the two classes of

curves eventually do converge in adulthood.

STEP 5: STUDYING DYNAMIC DETERMINANTS

ACROSS MULTIPLE VARIABLES

In recent research, we have considered some ways to improve the clarity of

the basic dynamic change interpretations with conventional SEM analytic

techniques. This has led to the development of a set of alternative models,

based on classical principles of dynamic growth and change (including de-

cline), but represented in the form of latent difference scores (e.g., McArdle,

2001; McArdle & Nesselroade, 1994). This alternative representation makes

it relatively easy to represent a dynamic hypothesis about the change within

a variable, and about the time-ordered determination of one variable upon

another. An overview of this new approach is described here as the fifth step

in longitudinal data analysis.

Modeling Latent Difference Scores

The introduction of multiple variables at each longitudinal occasion of meas-

urement leads naturally to questions about time-dependent relationships

JOHN J. MCARDLE344

among growth. A classical SEM for multiple variables over time is based on a

latent variable cross-lagged regression model (see Cook & Campbell, 1979;

Rogosa, 1978). This model can be written for latent scores with over-time

auto-regressions (fy, fx) and cross-regressions (dyx, dx) for time-lagged pre-

dictors, but the standard applications of this model do not include systematic

growth components (i.e., individual slopes). For this reasons, recent SEM

analyses have examination of parallel growth curves, including the correlation

of various components (McArdle, 1988, 1990; Willett & Sayer, 1994). A

popular alternative used in multi-level and mixed-effects modeling is based on

the analysis of covariance with X[t] as time-varying covariates. In this model,

the regressions (d) are fixed (group) coefficients with the same effect on

Y[t] scores at all occasions. These last two models are easy to implement

using existing computer software (e.g., Sliwinski & Buschke, 1999; Sullivan,

Rosenbloom, Lim, & Pfefferman, 2000; Verbeke, Molenberghs, Krickeberg,

& Fienberg, 2000), but the typical applications are limited to a few basic

forms of dynamic hypotheses.

To expand our SEM for other dynamic concepts we start in a different

way. First, we assume we have a pair of observed scores (Y ½t� and Y ½t� 1�)

measured over a defined interval of time (Dt ¼ 1), and we write a model with

latent scores (y½t� and y½t� 1�), and corresponding errors of measurement

(e½t� and e½t� 1�), so the new latent variable Dy[t] is directly interpreted as a

latent difference score. This latent difference score (Dy½t�n) is not the same as

an observed difference score (DY ½t�n), because the latent score is considered

separate from the removal of the model-based error component. In this

latent difference score approach, we do not directly define the A[t] coeffi-

cients, but instead we directly define changes as an accumulation of the first

differences among latent variables.

This deceptively simple algebraic device allows us to generally define the

trajectory equation as an accumulation of the latent changes (Dy½t�) up to

time t based on any model of change. Using this approach, all of the previous

latent growth models can be drawn in terms of first differences, and some

new models can more easily emerge (as in McArdle & Nesselroade, 1994;

McArdle, 2001; McArdle & Hamagami, 2001). For example, we can write a

composite change expression model, where we permit both a systematic

constant change (a) and a systematic proportional change (b) over time.

This linear difference model lead to a nonlinear mixed-effects model tra-

jectory from a simple accumulation of first differences. It can also be fitted

using standard SEM software (e.g., LISREL, Mx, etc.)

To deal with multiple variables we can now write a bivariate dynamic

change score model, such as the model depicted in the path diagram of Fig. 9.


V3

e4 e5

Y[3]

y[1] y[2]

Y[4]

y[4] y[5]

Y[6]

y[6]

Y[5]

e2ey[1] ey[2] ey[4] ey[6]

y[3]

∆y[2] ∆y[3] ∆y[4]∆y[5] ∆y[6]

Y[1] Y[6]

∆x[2]∆x[3] ∆x[4]

∆x[6]

e4 e5

x[1] x[2] x[4] x[5] x[6]x[3]

V3X[3] X[4] X[6]X[5]X[1] X[6]

e2ex[1] ex[2] ex[4] ex[6]

y0

ys

xs

x0

∆x[5]

y0

ys

xs

x0

K

µy0

σy0,ys

µys

µx0

µxs

σxs

σx0

σys

σy0

σx0,xs

σys,xs

σy0,xs

σx0,ys

σx0,y0

αy

αx

βy βy βy βy βy

βx βx βx βx βx

γxγx γx γx

γx

γy γy γy γy γy

σy

2

σx2

σy,x

*

*

*

*

Fig. 9. A Bivariate Latent Difference Score (BLDS) Path Model for Dynamic Hypotheses.

JOHN

J.M

CARDLE

346

We assume a dual change score model within each variable (parameters a and

b) but also permit a coupling hypothesis (parameters g) across different var-

iables. This model is used to estimate the time-dependent effect of latent x[t]

on Dy[t] (gyx) as well as coupling parameter representing the time-dependent

effect of latent y[t] on Dx[t] (gxy). This model subsumes all aspects of the

previous cross-lagged, correlated growth, and time-varying covariate models

as special cases and this is useful because results from these dynamic alter-

natives can be quite different (see McArdle & Hamagami, 2001). These latent

difference score models can lead to more complex nonlinear trajectory equa-

tions (e.g., non-homogeneous equations), but the use of latent difference

scores makes it practical to analyze a variety of dynamic models using stand-

ard SEM.

Results from Fitting Latent Difference Score Models

The latent difference score dynamic models were fitted (using Mx and

Mplus) to both the General Knowledge variable (now termed Verbal) and

also to a second set of Non-Verbal scores. We will only highlight the dy-

namic results here (but see McArdle & Hamagami, 2004). In order to fit the

dual change model the additive slope coefficient was fixed for identification

purposes (a ¼ 1) but the mean of the slopes was allowed to be free (m1). This

allowed estimation of: (a) auto-proportion effects (b ¼ �0:34;�1:38), (b)initial level means (m0 ¼ 14:8; 32:1) at Age ¼ 5, and (c) linear slope means

(m1 ¼ 26:0; 110:1) for each 5-year-period after (Age 4 5). The goodness-

of-fit of the dual change model was compared to every other nested alter-

native and these comparisons show the best fit was achieved using this

model (e.g., versus a linear growth, w2 ¼ 584; 439 on df ¼ 1).

Several alternative Verbal–Non-Verbal bivariate coupling models based

on Fig. 9 were fitted to the data. A first model included six dynamic co-

efficients (two each for a, b, g), four latent means (m), six latent deviations

(s), and six latent correlations (r). This model was fitted with N ¼ 111

individuals with at least one point of data, 498 individual data observations,

and yield one overall fit (�2logL ¼ 7118), which was different from a ran-

dom baseline (w2 ¼ 379 on df ¼ 16). The model parameters can be used to

form the expected values described in Fig. 9. These parameters are specific

to the time interval chosen (i.e., Dt ¼ 5) and any calculation of the other

information (e.g., explained latent variance) requires a specific interval of

age. However, these seemingly small differences can accumulate over longer

periods of time so the larger N[t] is expected to account for an increasing

variance in DV [t] over age.


The fitting of a further sequence of alternative models is needed to in-

terpret the replicability of the coupling across the V [t] and N [t] variables.

Table 6 gives details about two specific models fit to examine whether one or

more of the coupling parameters (g) were different from zero. In the first

alternative model (6a), the parameter representing the effect of N[t] on DV[t]

was fixed to zero (gx ¼ 0), and this led to a notable loss of fit (w2 ¼ 123 on

df ¼ 1). The second alternative (6b) assumed no effect from V ½t� on

DN½t�ðgy ¼ 0Þ and this is a much smaller loss of fit (w2 ¼ 27 on df ¼ 1).

These results were examined in a number of results of additional ways, but

these comparisons suggest that the second model 6b was the most reason-

able representation of these longitudinal data. The resulting interpretation is

a dynamic process, where scores on Non-Verbal abilities have a tendency to

impact score changes on the Knowledge-Verbal scores, but there is no no-

table reverse effect.

The estimated model parameters are highly dependent on the scalings

used, but the trajectory expectations allow us to interpret the results in a

relatively ‘‘scale-free’’ form – Fig. 10 gives a summary of this state-space

plot as a vector field plot (for details, see Boker & McArdle, 1995; McArdle

et al., 2001). Any pair of coordinates is a starting point (y0, x0) and the

directional arrow is a display of the expected pair of 5-year changes (Dy, Dx)

from this point. The last two figures show an interesting dynamic property –

the change expectations of a dynamic model depend on the starting point.

From this perspective, we can also interpret the positive level–level corre-

lation (ry0;x0 ¼ 0:78), which describes the placement of the individuals in the

vector field, and the small slope–slope correlation (rys;xs ¼ �0:06), whichdescribes the location of the subsequent change scores for individuals in the

vector field. The resulting ‘‘flow’’ shows a dynamic process, where scores on

Non-Verbal abilities have a tendency to impact score changes on the Verbal

scores, but there is no notable reverse effect.

DISCUSSION

This chapter can only serve as an introduction to a general class of pro-

cedures that can be classified under the rubric of ‘‘latent growth curve

modeling techniques.’’ The brevity of this chapter cannot do justice to the

broad applicability of these techniques, and the five steps outlined here

represent just one way to organize some of the inherent complexities of this

topic. In this last section, I discuss some of the future directions and chal-

lenges for this kind of research.

JOHN J. MCARDLE348

Table 6. Results of Bivariate Latent Difference Score (BLDS) Dynamics Models Fitted to Two Variables

over Six Occasions (for details, see McArdle & Hamagami, 2004a, b; Step 5, see Figs. 9 and 10).

Model Parameters 6a (No Coupling from Non-Verbal) 6b (No Coupling from Verbal)

Knowledge Non-Verbal Knowledge Non-Verbal

(a) Fixed effects

Initial mean m0 14.8 32.1 15.5 31.8

Slope mean ms 26.1 42.4 0.01 46.7

Loading a ¼ 1 ¼ 1 ¼ 1 ¼ 1

Proportion b �0.34 �0.40 �0.77 �0.58

Coupling from knowledge gnk — ¼ 0 — 0.70

Coupling from non-verbal gkn �0.15 — ¼ 0 —

(b) Random effects

Error deviation se 7.04 10.5 5.85 10.8

Initial deviation s0 2.87 2.84 1.64 4.92

Slope deviation ss 4.29 5.52 8.82 6.34

Correlation r0s o0.99 0.40 o0.99 0.52

Correlation ry0, x0 o0.99 o0.99

Correlation rys, xs 0.63 0.39

Correlation ry0, xs 0.44 o0.99

Correlation rys, x0 0.85 �0.39

(c) Goodness-of-fit

�2log L 7241 7145

Parameters 19 19

Five

Step

sin

Laten

tCurve

Modelin

gwith

Longitu

dinalLife-S

panData

349

Formal models for the analysis of these kinds of growth curves have been

developed in many different substantive domains. Researchers have found

many creative ways to analyze average trends in growth data, including

classical Analysis of Variance techniques (e.g., Pothoff & Roy, 1964; Bock,

1975). These classical methods provide powerful and accurate tests of

‘‘group trends.’’ However, the introduction of individual differences in

change analyses led to a great deal of statistical controversy in model fitting.

Early work on these problems led to the polynomial growth models by

Wishart (1934), where an individual regression coefficient was used to de-

scribe a growth characteristic of the person (see Rogosa & Willett, 1985).

Other current techniques have roots in the important innovations by

Meredith & Tisak (1990), who showed how the ‘‘Tuckerized curve’’ models

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

Non-V

erbal

Verbal

Fig. 10. Results from the Vector Field Representation of the BDLS for the Six-

occasion Longitudinal Data. (Note: Each Arrow Represents the Expected Change of

Direction in the Scores – the Derivatives – for Pairs of Initial Starting Abilities.)

JOHN J. MCARDLE350

(named in recognition of Tucker’s contributions) could be represented and

fitted using structural equation modeling based on restricted common fac-

tors. For these reasons, the term latent curve models (LCM) seems appro-

priate for any technique that describes the underlying growth in terms of

latent changes using the classical assumptions (e.g., independence of errors).

These innovative techniques were important because this made it possible to

represent a wide range of alternative growth and change models by adding

the benefits of the structural equation modeling techniques (Meredith &

Tisak, 1990; McArdle & Prescott, 1997; McArdle, 1986; McArdle & Epstein,

1987; McArdle & Anderson, 1990; McArdle & Hamagami, 1992, 2001).

During the last decade it has become possible to prove that many seem-

ingly different statistical models have identical properties and can yield

identical results. They are all based on fitting observed raw-score longitu-

dinal growth data to the same theoretical model using the same likelihood-

based techniques (as in Little & Rubin, 1987; McArdle, 1994; McArdle &

Bell, 2000). These latent growth models have since been expanded upon and

described by many others (McArdle & Woodcock, 1997; Willett & Sayer,

1994; Muthen & Curran, 1997; Metha & West, 2000). The contemporary

basis of latent curve analyses can also be found in the recent developments

of multi-level models (Goldstein, 1995; Bryk & Raudenbush, 1992) or mixed-

effects models (Laird & Ware, 1982; Singer, 1998). In important work by

Browne & du Toit (1991), classical nonlinear models were added as part of

this same framework (see Cudeck & du Toit, 2001; McArdle & Hamagami,

1996, 2001; Pinheiro & Bates, 2000). When these options are added to the

latent variable path analysis models of SEM (e.g., McArdle & Prescott,

1992), many limitations apparent in previous longitudinal research can be

overcome.

In a similar way, most of the models presented here can be estimated

using contemporary computer programs including: (1) the SAS and S-Plus

packages (Littell et al., 1996; Singer, 1998; Verbeke & Molenberghs, 2000;

Pinheiro & Bates, 2000), (2) general SEM programs such as LISREL

(McArdle & Epstein, 1987; Cudeck & duToit, 2001), Mx (Neale, Boker, Xie,

& Maes, 1999), Mplus (Muthen & Muthen, 2002), and AMOS (Arbuckle &

Wothke, 1999), and (3) specialized softwares such as MIXOR (Hedecker &

Gibbons, 1996) and MLn (Goldstein, 1995). The results for the classical

SEM for latent curves will be the same no matter what program is used (for

demonstration, see Ferrer, Hamagami, & McArdle, 2004). Some of the more

complex models used here (e.g., Table 6) now require specialized SEM

software (e.g., Mx or Mplus), but it is likely that these routines will become

a part of standard computer packages in the near future.


At the same time, I need to recognize that these SEM developments

represent a limited class of longitudinal data analyses (e.g., Nesselroade &

Baltes, 1979; Collins & Sayer, 2001), and we should be on the lookout for

possible improvements. In the analyses presented here, I have also tried

to present some of our most up-to-date interpretations dealing with the

developmental-dynamic processes around aspects of human cognition over

the life-span. Indeed, some of the most difficult problems for future work

on latent curves will not involve SEM statistical analysis or computer

programming, but will be focused on the rather elusive meaning of the la-

tent model parameters themselves (Zeger & Harlow, 1987; McArdle &

Nesselroade, 2003). These substantive-methodological problems remain

among the most difficult dynamic challenges for our future work.

ACKNOWLEDGMENTS

The work described here has been supported since 1980 by the National

Institute on Aging (Grant#AG-07137). I am especially grateful to the work

of my close friend and colleague, Fumiaki Hamagami, and to my close

collaborations with Katherine P. Bradway and John L. Horn. This research

was also helped by the support of my many friends and colleagues, including

Steven Aggen, Paul Baltes, Steven Boker, Emilio Ferrer-Caja, Paolo

Ghisletta, Patty Hulick, Bill Meredith, John Nesselroade, Carol Prescott,

and Dick Woodcock. I also thank the PaVie team for excellent editorial

suggestions and assistance in the presentation and preparation of this man-

uscript. Reprints can be obtained from the author at the Jefferson Psycho-

metric Laboratory, P.O. Box 400400, Department of Psychology, University

of Virginia, Charlottesville, VA 22904, USA.

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358

AFTERTHOUGHTS

359


360

INCITATIONS FOR

INTERDISCIPLINARITY IN LIFE

COURSE RESEARCH

Rene Levy, Paolo Ghisletta, Jean-Marie Le Goff,

Dario Spini and Eric Widmer

HOUSING THE HARVEST

Having gone through this volume, a critical reader might come to the con-

clusion that interdisciplinarity can be found more easily between the con-

tributions than within them (even though several of them address it directly,

e.g., Settersten & Gannon; Mortimer et al.).1 However, the contributors

share the common belief that studying humans’ unfolding lives in a web of

complicated interactions within their changing contexts requires the adop-

tion of an interdisciplinary research paradigm. To be sure, the life-span/life

course research traditions stemming from disciplines such as sociology,

psychology, social psychology and demography certainly have allowed

scholars to answer some key questions germane to this field (Baltes,

Lindenberger & Staudinger, in press; Elder, 1998). The empirical evidence

accumulated over the years contributed heavily to the validity of the en-

terprise represented by life course research (Baltes, Reese, & Nesselroade,

1977). The development toward interdisciplinarity needs, however, not only

solid disciplinary foundations and the shared wish to cooperate, but also





ISSN: 1040-2608/doi:10.1016/S1040-2608(05)10013-6

361

hard and time-consuming work in interdisciplinary groups to progress con-

cretely in this direction, possibly along the three lines sketched out in our

introduction: constructing theoretical bridges between disciplinary ap-

proaches, building on common concepts that help describe and analyze

life courses, and working on transversal substantive themes.

In this final chapter, the editors take up the four thematic groups of

contributions, agency and structure, transitions, biographical reconstruction

and methodological innovations, in order to scan them for elements that

seem instrumental for further building up interdisciplinarity in life course

research.

INDIVIDUAL AGENCY WITHIN SOCIAL

STRUCTURE, AND STRUCTURAL AGENCY

Defining agency as the capacity to act intentionally, planfully and reflexively

in a temporal and biographical mode (Marshall, Settersten & Gannon)

points to the fact that the enactment of agency is, among other things, a

cognitive and emotional process, informed by the social environment, and

unfolding through time.2 Following Marshall’s argument, agency should be

distinguished from various related concepts, such as useful resources for

action, social action itself, intentions that motivate behavior, the social and

physical structuring of choices and unexplained variance.

Structure, on the other hand, is usually defined as the set of social con-

straints and opportunities within which individual agency plays out. Con-

tributions to this volume show that structures and agency form a system of

interrelated elements rather than a chain of distinct factors with a clear

causal ordering, for instance, from the macrosocial down to psychobio-

logical levels, or inversely, from cognitive skills upward to social structures.

We conclude from these contributions that social structures have agency of

their own, that agency has structures of its own, and that both should be

jointly looked at when studying life course issues.

To start with the first of these two combinations: social structures, in

some sense, have agency, too. States, as well as firms and other institutions

whose functioning has direct implications for individual life courses, are

shaped by human beings, be they simple members, clients or institutional

decision-makers, with purposes, goals, and agency. The latter, institutional

entrepreneurs in the sense of Eisenstadt (1968), put up programs to instan-

tiate their views about the life course of their citizens, patients or employees.

RENE LEVY ET AL.362

The agency of institutional entrepreneurs is transformed into structures that

both bound and orient the agency of other actors. A promising line of

inquiry in life course research concerns the way in which various institu-

tional actors participate in the structuring of the life course in democratic

societies. European sociology, especially in Germany (for instance, Mayer &

Muller, 1986; Mayer & Schoepflin, 1989) has much emphasized the impor-

tance of the modern State (with all its agencies, including the education

system and the law) and the development of the market economy in this

regard, leading to trends of standardization, individualization and sexual

typification of individual trajectories (Kohli, 1985, 1986; Kruger & Levy,

2001). Social historians show that these changes responded in many cases to

instrumental thinking and action of political leaders, such as Bismarck in

Germany, Lord Beveridge in the United Kingdom, or Roosevelt with the

New Deal (Elder, 1974). Structural changes therefore are often led by in-

stitutional agency, a fairly complex and constrained process that does not

only depend, in democratic societies, on a few institutional entrepreneurs,

but also on various political forces, including social movements – that can

themselves be analyzed in a life course perspective. Following Berger and

Luckmann’s (1966) line of thought, institutional agency may then be ob-

jectivated, i.e., become a constraining social fact, which may later be in-

teriorized by individuals who incorporate it when making life course plans.

On the other hand, agency has structures of its own, both psychological

and social. Narratives usually have a temporal, causal and thematic coher-

ence. Goals and motives are correlated with psychological traits (McAdams).

Life insight of individuals, that is, how to act for one’s own good and the

good of others, is deeply intertwined with personality and cognition abilities.

Agency as a cognitive process is therefore strongly shaped by psychological

characteristics, such as resources of various kinds, as studies of the effects of

personality on memories show (Perrig-Chiello & Perrig). This capacity to act

or react, which developmental psychologists sometimes refer to as resilience

or coping whereas sociologists may rather speak of planful competence (as

Settersten and Gannon remind us), displays various complementary strat-

egies, such as the selection–optimization–compensation or SOC strategies

found in relation with the aging process (Baltes & Baltes, 1990).

Agency seen as a cognitive process matters a lot for social behavior and

social structures throughout the life course. For instance, as Mortimer et al.

show, adolescent goals are transformed into part-time work habits, which

later unfold in distinct ways of entering the job market. Work on the effect

of early childbearing provides similar results. Contrary to social determin-

istic approaches, like the statement that ‘‘The girl who has an illegitimate

Incitations for Interdisciplinarity in Life Course Research 363

child at the age of 16 suddenly has 90 percent of her life’s script written for

her’’ (Campbell, 1968), Furstenberg points to the fact that non-normative

events produce diverse consequences over the life course depending on

mental health, cognitive skills, motivation and flexibility of individuals.

Settersten and Gannon provide examples from various phases of the life

course (childhood and adolescence, early adulthood, old age, etc.) in which

social constraints are dealt with in creative ways by individuals. Thus agen-

cy, as a cognitive process, makes a difference for individual lives.

Agency, however, is not only constrained or enhanced by psychological

and macrosocial factors, but also by microsocial factors unfolding through

individual time. Rather than considering agency as a black box, not to be

opened in empirical analyses, some sociologists propose to see it as path

dependent in a specific sense: narratives, goals, motivations and cognitive

abilities are shaped to a large extent by the actual trajectories themselves

(Abbott, 1992). In this regard, the long-time experience of social psychol-

ogists in dealing with identity formation in social contexts is particularly

useful.

In a second perspective (Emler), narratives, life wisdom and goals are not

constructed by isolated actors: they emerge in connection with narratives,

life wisdom and goals of significant others, in social networks in which

various agencies shape each other. Agency is not only an individual phe-

nomenon, but also a collective one (Settersten and Gannon): studying co-

agency between connected people (especially in gendered relationships) is

addressing the central issue of linked lives (Elder, 1996) in a novel and

interdisciplinary way.

More generally, a fruitful option for dealing with the agency–structure

debate suggested by this volume is to consider both of them as variables in

interaction rather than as main effects to be statistically controlled for. The

concept of ambivalence (Luscher) is especially helpful in this respect.

Luscher suggests four basic ways of experiencing and dealing with inter-

generational ambivalence within family relationships, with specific cou-

plings of agency (the subjective dimension of the model) and structure (its

institutional dimension): solidarity, emancipation, captivation and atomi-

zation. At one end of the spectrum, solidarity is defined as a situation in

which family members feel subjectively committed to the maintenance of

institutionalized patterns of help and relationships, while, at the other end of

the spectrum, atomization corresponds to cases in which family cohesiveness

is neither subjectively nor institutionally assured. According to Luscher, we

may expect the various types of interaction between agency and structure to

be useful for the understanding of turning points in the life course, such as

RENE LEVY ET AL.364

the transition to parenthood, which are associated with both new con-

straints and new roles. Specific life stages, such as old age with its increasing

tension between autonomy and dependency, may be especially conducive to

ambivalence. This may be particularly true if we agree with Settersten and

Gannon’s statement that the life course has become at a time more stand-

ardized and more destandardized in recent decades, a situation that may

lead to increased ambivalence throughout the life course.

Because of the emerging acknowledgement of the intricacy of the rela-

tionship between agency and social structure as variables, there is a growing

need for interdisciplinary approaches that may help to capture the interplay

between agency as a cognitive process with its own set of psychological and

microsocial constraints, and social structures, which are partly the result of

institutional agency. This implies a stronger partnership between life-span

psychology, social psychology, social demography and life course sociology.

As suggested by several contributions, sociologists and social demographers

often overlook the role of personality traits and identity narratives when

explaining individual trajectories, while psychologists, developmental or

social, often disregard contextual factors, in particular those stemming from

the historical context in which individuals are embedded. The contributions

to this volume suggest some ways in which sociologists may unfold the

richness and intricacies of the ‘‘homo psychologicus’’ and include its ele-

ments in their explanatory models of the life course. Likewise, it suggests

how life-span psychologists may include more sophisticated and refined

conceptualizations and measures of the social contexts in their research.

TRANSITIONS AS SOCIAL AND

PSYCHOLOGICAL ‘‘ANALYZERS’’

Life courses go through and are marked by transitions. Transitions and the

stages between them define each other mutually, not only in the formal sense

of stages being bounded by transitions and transitions being inserted be-

tween stages, but also in a more substantive sense: most transitions are being

prepared, anticipated and influenced in many ways during the phases pre-

ceding them, and influence in turn the phases that follow. Transitions may

be ‘‘normative’’ in psychological language or, as sociologists would prefer to

say, modal or statistically predominant, they may also be socially considered

to be desirable, possibly indispensable, or they may be non-normative or

even deviant with respect to social norms, as in the case of the teenage


mothers and divorced women discussed by Furstenberg. Some transitions

belong to the ‘‘normal’’ course of peoples’ lives and contribute to their

regular ‘‘progress’’, while others may throw a life off its rails and become

turning points, biographical accidents provoking life course bifurcations.

Some transitions are relatively smooth, making little ‘‘unrest’’ for the per-

sons directly or indirectly concerned, others, even if ‘‘normative’’ in the

above sense, may have considerable and partly non-anticipated conse-

quences (such as the transition from a cohabiting couple to a family with a

first child; Widmer, Levy, Pollien, Hammer, & Gauthier, 2003b; Widmer,

Kellerhals, & Levy, 2005; Moen & Han, 2001).

Even if it is somewhat reductionist, life course transitions may be seen as

moments of important change, in contrast to the periods between them that

are rather marked by stability, especially if we think of social–psychological

and psychological correlates. These should not be underestimated because,

as we know, individuals are not only socialized actors with ‘‘internal’’ cul-

tural competencies, they are also field- or context dependent in many re-

spects (identity, self-esteem, etc.), even if these dependencies may themselves

vary within (see, for example, Kernis & Goldman, 2003; Nesselroade, 1991)

and between individuals, and also across life phases. For this reason, tran-

sitions are likely to be in many respects more critical for the person than

stable periods.3 As a person’s situation changes, he or she finds him- or

herself in new conditions that are different from the preceding ones, not only

in terms of being more or less advantageous, more or less restrictive for

spontaneous initiatives, etc., but also in terms of their quality or nature. In a

sociological perspective, this is reason enough to postulate a series of con-

sequences that are not only of a sociological order, but concern also the

levels of social-psychological and psychological analysis. The following four

paragraphs are an attempt at formulating interdisciplinary hypotheses along

this line.

A first, highly probable corollary concerns identity, for oneself as well as

for others, as several contributors remind us (especially Marshall and

Emler): after a transition, a person will typically assume other roles, respond

to other expectancies, have other rights and duties and interact with other

people on other terms than before. According to the number and impor-

tance of the role shifts implied in a transition, this change will be more

unsettling or even dramatic, or less. It will affect the person’s self-image, the

representations others have of him or her and also her or his actively per-

formed self-presentation to others. Identity may appear as less settled during

a transition and more put into question, open to various directions and

influences, also to voluntaristic self-influence.

RENE LEVY ET AL.366

Second, taking up one of Kohli’s (1985, 1986) theses, we may ask our-

selves whether on the individual level transitions are also moments of

heightened ‘‘biographization’’, i.e., moments in which the transiting life-

passengers have an increased sense of their being themselves the construc-

tors of their life. It is, of course, also easy to figure out the contrary:

transitions that rather restrict opportunities of action are more apt at elic-

iting sentiments of being passively channeled rather than actively piloting

one’s life. This means at any rate the question of agency will be of particular

relevance in transitions, as particularly well highlighted by Furstenberg’s

comments on the variability of the outcomes he finds for non-normative

transitions, but also by the analyses of Mortimer et al. Do we know enough

about variations of persons’ control beliefs across major and minor bio-

graphical transitions, and about their consequences?

A third, more general hypothetical corollary can be seen as an extension

of the first one: it is more than plausible that throughout the life span, life-

long socialization is not continuous but rather rhythmical, being boosted by

life course transitions and slowing down during the phases in-between, be-

cause in transitions familiar contexts of reference loose relevance and are

replaced by new, less well-known ones. One may extend this argument to

integrate the apparently contradicting ideas about when major phases of

socialization take place in a lifetime: birth and the very first years of life

represent the first biographical transition of a human being, the person’s

first entry into a social field,4 and this field’s active and passive exploration

while being in a particularly fragile – and therefore subjectively highly

significant, affectively ‘‘mobilizing’’ – situation. Consequently, the social-

ization taking place in these circumstances is bound to be particularly im-

pressive and rich in consequences. In comparison, later spells of socializat-

ion, e.g., in adolescence, concern an already (at least partly) structured, i.e.,

socialized person and need considerable social and psychological weight to

supersede or basically relativize the elements already acquired in prior so-

cialization processes; moreover, it concerns an individual that is already

‘‘biographically constructed’’ and becomes, at least potentially, an active

and critical participant in her/his own socialization. We may see these el-

ements as a background to some of Furstenberg’s arguments, based on the

idea that earlier socialization may be part of selection processes (e.g., prob-

lematic socialization outcomes increase peoples’ chances of ending up in

out-of-schedule situations) that create some of the unobserved variation in

results about negative consequences of non-normative transitions, leading

to simplistic or at least hasty conclusions about the negative consequences of

such non-normative transitions in women’s or children’s life courses.


A fourth corollary of transitions is related to the management of the

change they imply. Transitions will be envisaged by the concerned persons

with more apprehension than the stable periods between them, possibly also

with more ambivalence (Luscher) because they represent – to varying de-

grees, of course – potential risks in a person’s biography. The ways actors

themselves and their social environment handle these risks and the subjective

insecurity they entail is an obviously important theme for life course re-

search; this theme can be conceptualized in terms of individual and collective

adaptation or coping. There may be a vast array of forms and resources for

coping related to diverse transitions, some of them specialized, some of them

of a general nature: rituals, other forms of dramatization (including private

ones), emergence of professionalized transition specialists (gate keepers as

well as transition helpers), problem-solving literature for both professionals

and lay persons, repair institutions (such as hospitals, rehabilitation clinics,

training institutions for vocational reorientation, etc.), but also general re-

sources and styles of coping that are not situation-specific.

All these arguments point to the fact that life course transitions are not so

much instantaneous moments of switching from one situation to another –

Bird and Kruger warn us against a ‘‘guillotine-like perception of transi-

tions’’ – but rather processes that may be of considerable duration and

complexity. Transitions, if they are not triggered by unexpected events, are

mostly prepared for and subject to anticipation. They may be composed of

several differentiated changes, and can imprint their reality on the person’s

everyday life progressively. All of this does not happen in a tick, but takes

biographical time. This reason is enough in itself to justify Elder’s (1998)

insistence on the necessity to consider each transition as a series of mini-

transitions or decisional moments; Levinson’s (1990) concept of transitional

periods may often be more appropriate than the simple term of transition;

the same holds a fortiori for the term of event.

Another important aspect is the way in which transitions are embedded in

the whole trajectory of a person: what are the factors that trigger and mod-

ulate a transition to begin with, and what consequences do transitions and

their outcomes have for the subsequent trajectories? How can we theorize the

oft-cited cumulativity of life course developments? Let us mention just one

example. According to research in several – especially European – countries,

the transition from the situation of a cohabiting couple without children

(pre-child phase) to the one of a family with a pre-school child is quite

systematically accompanied by a switch of the couple to a more traditionally

gendered task organization, relatively egalitarian convictions of the partners

notwithstanding (Li & Currie, 1992; Born, Kruger, & Lorenz-Meyer, 1996;

RENE LEVY ET AL.368

Kalicki, Fthenakis, Peitz, & Engfer, 1998; Kalicki, Fthenakis, & Peitz, 1999;

Widmer, Kellerhals, & Levy, 2003a; Widmer et al., 2003b). This

‘‘retraditionalization’’ of the families’ internal structure seems to be quite

resilient in later family phases and is not strongly influenced by the female

partner’s degree of resuming paid work. It may well be that it is precisely in

analyzing closely how transitions are initiated and produced, and how they

produce in turn their consequences, that we will be able to better understand

how the agency-in-structure vision advocated by Settersten and Gannon can

be more finely modeled theoretically.

Several contributions rightly underline the variability and complexity of life

courses and specific transitions (especially Furstenberg and Mortimer et al.)

and of diverse factors to be uncovered behind this variability (Mortimer et al.,

Marshall). Bird and Kruger remind us with a powerful argumentation that

matters are more complex than a merely sequential conceptualization might

suggest, because the complexity of transitions is not restricted to this ‘‘linear’’

aspect. This is what they call ‘‘inline’’ transitions, which have to be completed

by adding to the overall picture also ‘‘competing’’ and ‘‘coupled’’ transitions.

They warn us usefully against several kinds of substantively inadequate

complexity reductions that lie in wait for researchers. We have not only to

take into account the fact that most transitions are not just events and that all

three types of transitions may occur together, but we should also resist the

simplifying ‘‘offers’’ suggested by some easygoing technical terms, such as

formal status definitions that may not fully coincide with the practical reality

of persons’ lives (see their examples of being married while living alone), or

technical terms like event history analysis.

An aspect that has long been at the core of life course analysis is what has

been generally called ‘‘the timing of events’’, including the precise timing of

important events and transitions as well as the duration of the phases be-

tween them, and often also the normative schedules concerning them (age

norms). Furstenberg shows convincingly that we should be much more

circumspect about the social and psychological meaning of transitions being

out-of-time or out-of-order. The fact that a life course does not replicate the

‘‘normative’’ pattern, when such a pattern exists, is ostensibly not enough in

itself to diagnose a problem for the future of the non-respectful life course

passengers. Furstenberg considers a series of additional conditions that are

likely to play a crucial role for such a situation to be socially and subjectively

problematic – or on the contrary enhancing, by way of mobilizing latent

potentials and resources. This is certainly a research area in need of further

conceptual refinement as well as interdisciplinary treatment, and touching

directly on agency, for that matter.


What about transitions in a psychological or social–psychological per-

spective? Stage models, which would seem to be prima vista candidates for

psychological equivalents of social transitions, however prominent they have

been in the developmental psychologies of authors like Piaget, Kohlberg or

Erikson, are not supported by research on cognitive functioning. The topic

may be more promising with respect to identity changes, in line with our

above hypotheses. The weak or non-existent empirical basis of psychological

stage models is also one of the main reason for the absence of stages in

Baltes’ SOC model of life-span development (see next section). This situation

on the side of life-span psychology contrasts strangely with many life course

sociologists’ stressing the timing of life events. The idea of more or less

abrupt endogenous changes is put into question in a significant part of life-

span psychology in favor of a gradual view of age-related changes. In some

respects similarly, life course sociology shows a strong commitment to con-

sidering age as the basic dimension for the study of life-long development,

even if it seems to have some difficulties to conceptualize its meaning

(Settersten & Mayer, 1997); however, more than psychology, sociological

approaches insist strongly on events or transitions. Can these views be

brought together? Maybe a renewed attention to transitions and their var-

ying (and not necessarily close) relationship with age is in order from both

the psychological and the sociological side, developing a stand that is less

‘‘naturally’’ and immediately oriented toward age (without neglecting it) and

theoretically more outspoken about what can provoke change. We shall

push forward on this tread in the section about interdisciplinarity.

PERSONALITY, BIOGRAPHICAL

RECONSTRUCTIONS AND THE LIFE COURSE:

TOWARD A SYSTEMIC AND DYNAMIC APPROACH

Personality and Identity Across the Life Course

As already mentioned, the relationship between agency and structure is a

central theme of the sociological approach to the life course. However, as

often underlined by sociologists, psychologists tend to neglect the impor-

tance of the structural components of the life course. The inverse is also true;

psychologists often consider the theories of sociologists, which usually insist

on the motivation and goal orientation of the individual across the life

course, as oversimplifications with respect to their disciplinary knowledge.

RENE LEVY ET AL.370

Recent advances in the psychological and psychosocial study of the indi-

vidual across the life span are illustrated by several chapters of this volume

and have important implications for the development of a truly interdis-

ciplinary approach to the life course. Putting into perspective different as-

pects of such developments in personality and identity theory will enable us

to better articulate the different contributions of this volume.

Personality as a Framework for Studying Individuals across Time

In the framework of personality theory, there is some consensus about the

important dimensions of personality that must be distinguished and related

with each other. However, there are also fundamental debates, especially

around two questions that have to be tackled seriously by life course

theorists. The first concerns the interplay of biology and personality and the

second the question of the degree to which personality interacts with the

social environment and with trajectories in different life domains.

Since the seminal work of Allport (1937), personality psychology has been

broadly defined as the scientific study of the individual person, and per-

sonality development across the life span has been a central theme. Theories

of personality development across the life span have been proposed on the

basis of the psychodynamic tradition (Sigmund Freud & Carl Gustav Jung,

for example) and developed in an offspring known as life-cycle psychology

(Erikson, 1963, 1968; Havighurst, 1972; Levinson, 1978, 1996; Neugarten,

1977). However, these theoretical frameworks have been repeatedly criti-

cized for lacking empirical support to their theoretical claims.

More recently, alternative systemic models (Hooker & McAdams, 2003;

McAdams, 1996; McCrae & Costa, 2003; Mischel & Shoda, 1998) have

emerged and offer a synthesis of empirical results and theoretical develop-

ments. The model of McCrae and Costa (2003) is known as the Five-Factor

Theory (FFT). This model makes the assumption that our personality is

founded on basic tendencies, which are composed mainly by the Big Five

dimensions of personality (traits of neuroticism, extraversion, openness,

agreeableness and conscientiousness), but also by sexual orientations, some

cognitive abilities and artistic talents. These basic traits appear to be fairly

stable across the life course, influenced mainly by biological factors (some

changes appear but are described as predictably linked to age; see Srivatava,

John, Gosling, & Potter, 2003), and to influence the other two components

of personality across the life course, namely, the characteristic adaptations –

personal strivings, attitudes, worldviews, strategies or processes of coping


and adaptation (Baltes & Baltes, 1990; Brandtstater, Krampen, & Heil,

1993; Heckhausen, 1999) – and the self-concept system (self-schemas, iden-

tity, personal myths, life narratives). Contrary to the basic tendencies, these

two elements are based on learning and are subject to external influences like

cultural norms and life events. Finally, we find the objective biography,

which represents the main observable outcome of basic personality tenden-

cies and characteristic adaptations.

McAdams (1996 and this volume) proposes a comprehensive model of the

person distinguishing three levels (traits, personal action constructs or char-

acteristic adaptations, and the life story) that has been further elaborated in

a more recent version by Hooker (2002) and Hooker and McAdams (2003).

These authors’ new model, named the Six-Foci Model of personality (SFM),

is based on three structural components and three process-related compo-

nents (processes producing corresponding structure components). The three

structural components correspond to the three levels of personality already

described by McAdams (1996). The corresponding processes are respectively

states (emotions, moods, hunger, fatigue, anxiousness), self-regulatory

processes (especially primary and secondary control; see Schulz &

Heckhausen, 1996), and processes of self-narrating (remembering, reminis-

cence and storytelling). This new systemic model of personality, in comple-

ment with the FFT model, allows for a heuristic description of the aging

person and focuses researchers’ attention on the self and its relations

to contents (mainly present-oriented), goals (mainly future-oriented) and

reconstructions of biography (mainly past-oriented).

Personality, Identity and the Social Environment

Some of the main issues tackled in this volume concern, on the one hand,

relationships between different areas of personality and identity, and on the

other, how dynamics in personality and identity are related to dynamics in

the social environment. The relations between the different components of

the personality and identity systems are very complex; FFT and SFM mod-

els have different views in this respect. FFT postulates a causal chain of

influence starting from biology and traits, mediated by the experiences and

learning of the individual across the life span, which finally results in its

biographical trajectory. Clearly, here, differences in traits between individ-

uals are thought to influence the other dimensions of personality (identity

and characteristic adaptations). Perrig-Chiello and Perrig exemplify this

kind of model; they argue that the relationships between well-being and

RENE LEVY ET AL.372

autobiographical recollection or episodic memory are indeed influenced by

personality traits like extraversion, conscientiousness and neuroticism, as

predicted by FFT. However, as in other studies, the direction and strength of

this influence of traits, as well as its variability across domains of the per-

sonality structure, remains an open question (Mischel, 2004). Moreover, in

order to evaluate more precisely the relationships between life trajectories

and personality or identity structures and process, two additional issues

need to be developed in future research. The first is the necessity to combine

different methodological strategies for life course research (nomothetic–

idiographic; qualitative–quantitative, individual versus aggregated hierar-

chical data, etc.), with special attention to longitudinal designs. The second

is the need to articulate different levels of analysis in order to better

understand the relationships between the ‘‘objective’’ and ‘‘subjective’’

biography and between the individual, relational and collective levels. In

Perrig-Chiello and Perrig, for example, transitions and life events are re-

corded on the basis of memory, and the social context is simplified to a small

number of general variables (mostly gender and education). Sociologists and

social demographers would wish to include more detailed information on

the social position of individuals and on the institutionally monitored life

transitions. Life trajectories cannot be assessed only with individuals’ mem-

ory-based recollection as autobiographical memory, even in such areas as

job history or parents’ occupations (see Scott & Alwin, 1998), because they

are subject to memory biases and to well-known processes of reconstruction

that Perrig-Chiello and Perrig interestingly also develop in their chapter.

The chapter by Emler shows that the relationships between identity

changes and life transitions are indeed complex and should be modeled

more explicitly. On the basis of social-psychological research, he shows that

identity development does not correspond to a general pattern of qualita-

tively different hierarchical stages across the life span as postulated in the

Piagetian and in particular in the life-cycle tradition. His results indicate

that self-categorization processes are related to the social environment in

which they take place. Consequently, the content and processes related to

the self are primarily associated with time and location, two central dimen-

sions of the life course approach (Settersten, 1999). Emler shows also that

social identity is related to social relationships and locations, and that

changes in social identity (related to life transitions or turning points) are

often followed or anticipated by geographical moves leading to changes in

the social participation of individuals.

This systemic relationship between social identity (defined and construct-

ed through interaction with relevant others in different settings and types of


relationships) and personality on the one hand and the social and cultural

context on the other is also at the heart of McAdams and colleagues’ work,

as illustrated in their research about generativity in midlife. Generativity is

a central theme of identity, which is in turn related to a wide range of

engagements within society. It is in direct interaction with the social envi-

ronment, in which individuals continuously reformulate their identity sto-

ries. The life-stories orientation reminds us of an essential element in the

way individuals and life stories are related: meanings must not be ignored

when considering how the individuals interact with their social environment.

It is through meanings that individuals are related to their social networks,

to their engagements in different life spheres and to their life trajectories

(past, present and future). It is also through shared meanings or social

representations that individuals are related to ideologies and their socio-

cultural context (see, for example, the observations of Marshall on how

different institutions may create different stories and ideologies about death

and dying; see also Chryssochoou, 2003). As such, the two socialized com-

ponents of personality (characteristic adaptations and identity) are in con-

stant interaction with the social environment and biography. If individuals

are indeed active in the construction of the meaning they attribute to their

life, one conclusion of the chapters discussed here is that the social context

participates directly in the definition of the personality development of in-

dividuals by way of communication processes (socialization, social influ-

ence, conformity) and through the effects of non-normative events that

induce specific social–psychological processes of adaptation and coping

(e.g., in the sphere of health, Taylor & Brown, 1988; Taylor, Kemeny, Reed,

Bower, & Gruenewald, 2000). In this regard, social psychologists in some

way depict a more complex individual life course than the motivated actor

often seemingly referred to by sociologists.

To sum up, personality and social psychologists have developed models

like FFT and SFM that share the idea that three components should be

considered in any global comprehension of personal development. These

include, at the structural level, personality traits (relatively stable and struc-

tured early in the life course), psychosocial regulations (goals, secondary

control, coping processes, etc.) and identity (life story, self-categorization

processes, etc.). The relationships among these three components (and their

associated processes), and especially their relationships with the social

structure and the structure of the life course, are still in question. However,

personality psychology and social psychology have now developed a com-

prehensive model that should be useful for the interdisciplinary life course

perspective.

RENE LEVY ET AL.374

METHODOLOGICAL AND DATA-ANALYTICAL

APPROACHES

Collection of Life Course Data

Following Scott and Alwin (1998), we may distinguish two important issues

regarding the question of life-history data. The first is about what should be

measured, i.e., what kind of data to collect, while the second is about how to

do these measurements; it refers to data design.

Concerning the first, Scott and Alwin distinguish between three kinds of

measures in life course research: events, experiences and meanings. The term

of event refers to the collection of event data in different life domains (e.g.,

family, professional career) with the aim to analyze chronologies, sequences

or interactions between life events. This corresponds to the definition of a

life history given by Elder (1992): ‘‘a lifetime chronology of events and

activities that typically and variably combine data records on education,

work-life, family and residence’’ (p. 1122). Events are typically investigated

by demographers (Billari; Oris & Ritschard), to some extent also by soci-

ologists. The second approach measures cumulated experiences during an

individual’s past in order to analyze his/her present situation or his/her

expectations about the future. For example, the work experience of an ad-

olescent is considered as a predictor of the transition to the labor market or

to high school, as shown by Mortimer et al. The third kind of measure

focuses on the meaning or the evaluation a person attributes to her/his past

trajectory. This past can be contrasted with the present situation or future

expectations. The approach to narrative identity proposed by McAdams

exemplifies research collecting meanings of life courses.

Some contributions to this volume, and more generally most examples in

the literature, suggest that a fourth type of measurement in the life course

approach has to be considered, referring to the context or the institution-

alization of life courses. Data on context can be succinct information used as

a complement to life-event data and helps to depict the structure of con-

straints and opportunities in persons’ social environment. This kind of in-

formation is illustrated by Bird and Kruger by their taking into account the

legislation on motherhood leaves in Germany that influences the duration of

career interruptions. Context data include also interviews of life course

agents, i.e., persons who are present or give support at a specific stage or

transition in the life course, as developed by Marshall’s analysis of the

transition to death. One methodological difficulty is the integration of these

contextual informations and life course histories.


Concerning the second issue, two types of data collection design are cur-

rently used in life course research. The first one is the cross-sectional col-

lection of retrospective data. The reconstruction of individual life courses

with archive or administrative data forms a first subtype of retrospective

data. For historians interested in somewhat remote periods, this is practi-

cally the only possible strategy; its main disadvantage is that these data were

not originally collected for research purposes (Oris & Ritschard); research-

ers using them have no other choice than to accept them – and their lim-

itations – as ‘‘given’’ (Billari).

The second subtype of retrospective data is generated by surveys in which

persons are interviewed about events or experiences in their past. The quality

of data collected in retrospective surveys depends strongly on the memory of

respondents. Tools like life event history calendars minimize the potential

biases (Freedman, Thornton, Camburn, Alwin, & Young-DeMarco, 1988;

Belli, 1998). Retrospective data are often interesting to collect in order to

analyze the influence of a historical event or of a specific historical period

(economic crisis, war, etc.) on life courses. However, various studies on the

impact of a historical event on trajectories show generally that this impact

varies according to the situation or the stage in the life course where persons

are interviewed. A range of samples allowing to compare the impact of such

a historical event on trajectories in different generations, i.e., of people born

at different dates can be used in order to analyze this impact in terms of

cohort effects (Ryder, 1965). This strategy of combining the choice of design

and of sample characteristics is also appealing to analyze the effects of con-

textual changes (Bird & Kruger). However, retrospective data present several

limitations, the main one being that only factual data can be collected. This

implies that only information on events and experiences can be analyzed.

The other longitudinal design is the repeated collection of prospective

data. In psychology, this type of methodology is usually referred to as

longitudinal design, in other disciplines of the social sciences as panel design.

In this case, the same measures are applied to the same sample of persons at

various points in time. This kind of data collection allows taking into ac-

count current experiences as well as intentions about the future. This enables

life course researchers to confront intentions with their realization in sub-

sequent waves of the survey, or to investigate how expectations evolve

across the life course (Nurmi & Salmela-Aro, 2000). Panel surveys are es-

pecially interesting for research centered on agency. Several defaults of the

panel design have also been underlined, especially the attrition of cases

across succesive vawes of the panel or the risk to break time series if ques-

tions or experimental modalities are changed underway (MacArdle).

RENE LEVY ET AL.376

What might then be the best strategy for data collection in interdiscipli-

nary research? As mentioned above, events are the research topic favored by

demographers, historians, and, to a lesser extent, sociologists. Experience is

a topic common to all social science disciplines, but it is probably most

prominent in sociology and psychology. Meanings are a research topic of

particular interest to social psychology, but also to sociology. The necessity

to collect context data is more often invoked by demographers or socio-

logists than by psychologists. A preliminary condition of interdisciplinarity

is then to combine measures of events, experiences, meanings and context.

Moreover, a panel design allows to take into account each topic, especially

when it is completed by a qualitative survey. This type of panel design is also

especially interesting to analyze micromechanisms during a transition or a

change in the life course. It should be noted that retrospective surveys allow

also doing interdisciplinary work.

ANALYSIS OF LIFE COURSE DATA

Recently, certain data-analytical tools have come to play a critical role in

advancing knowledge about the life course. If integrated and combined,

these tools promise further interesting applications. We can identify four

general families of such tools that we consider especially helpful to study the

life course in an interdisciplinary perspective. While some of these tech-

niques have developed in parallel in different disciplines, others did so

mainly within a given discipline and may hence be less known by scholars of

other research fields. Life course scientists have a vital interest to get famil-

iarized with these methods even though some of them may seem rather

exotic at first sight. Some of them are directly treated by the contributions in

this volume, others are only hinted at, so it may be helpful to give a struc-

tured overview of this rapidly evolving array.

The first set of data-analytical tools we would like to mention falls under

the label of structural equation modeling (SEM) and has been adopted es-

pecially in psychological research. This set of techniques aims at explaining

the interrelationships observed among a set of chosen variables, usually by

the means of a correlation matrix. One of the major advantages of SEM

is that the researcher has complete freedom as to how to represent the

structure of the data. The researcher adopting SEM must define what the

underlying structure accounting for the interrelationships within the data

might be and translate that hypothesis (or series of hypotheses if several

alternative specifications are formulated) into a testable and rejectable


model, to be tested against the data at hand. This set of techniques further

has the desirable property of partitioning the variance of single variables or

indicators into a portion said to be common among the chosen variables,

hence representing latent constructs that cannot be observed nor measured

directly, but are thought to influence the measurable properties of the var-

iables, and a portion said to be unique to each variable. Hence, measure-

ment problems may often be circumvented with the application of SEM.

Some promising advanced applications of SEM to the study of the life

course are illustrated by McArdle’s contribution. There, changes between

adjacent repeated measures of a set of variables assessed on the same in-

dividuals are defined as latent differences, so that occasion-specific meas-

urement threats may be isolated and eliminated from the important

information gathered on the individuals. One may then test the existence

of variance in latent difference scores, which translates into interindividual

differences in change. Once differential change is established, correlates and

antecedents of change may be tested, one of the major goals of longitudinal

research (Baltes & Nesselroade, 1979).

A second set of techniques is that of event history or survival analysis

(EHA), adopted most heavily in demography (Billari and Ritschard & Oris).

The basic question motivating the application of EHA is what affects the

probability of the occurrence at a given time of a specific event (e.g., mar-

riage, birth of the first child, onset of a disease, death). Unlike SEM, a so-

called survival model does not have to be specified by the researcher. The

most popular example of EHA indeed is Cox’ proportional hazards model,

in which a semi-parametric hazard function provides very reasonable esti-

mates of the influences exerted by chosen covariates on the occurrence of the

event scrutinized. Particularly appealing is the test of time constancy of each

predictor (in practice unfortunately often overlooked). By introducing in the

model not only chosen predictors, but also their interactions with the un-

derlying time dimension, it is possible to test whether the potential effect of a

predictor holds across time or is manifested only at certain time periods.

Hence, what is believed to be an important predictor of the occurrence of an

event can be tested for its temporal relevance, much in the vein of what Bird

and Kruger remind us, namely that life course scholars ought to pay close

attention to the time dimension. Moreover, the regularity of time-ordered

events may also be assessed with this set of techniques.

A third set of techniques is that of multilevel models (MLM), also known

as random effects, mixed effects, and hierarchical linear models. This set

of techniques more than any other recognizes the possibility that the data

under inspection are structured according to either pre-defined (and usually

RENE LEVY ET AL.378

hierarchical) organizations (such as households within neighborhoods and

family members within households) or to configurations that were not

planned, but nevertheless resulted because of empirical research contingen-

cies (e.g., multistage sampling). Such situations do not meet the basic as-

sumption validating results from ordinary linear regression that the units of

observation are independent of each other, and ignoring this structure of the

data usually results in biased standard errors of the parameters (Goldstein,

1987). MLM can hence be conceived as regression equations with not one

source of variance (usually referred to as the errors, or the residuals), but

several sources of variance, the sources themselves being organized according

to the structure of the data (e.g., a first error may be associated with members

within a household and a second with households within a neighborhood).

Hence, variables at the individual and at the contextual levels can be modeled

together, making sure their distinctions are properly respected and not ar-

tificially removed.5 This approach is particularly promising for enriching the

‘‘agency and structure’’ discussion previously presented. Indeed, variables

measured at the individual (i.e., agentic) level can be analyzed in concom-

itance with variables assessed at the contextual (i.e., structural) level. More-

over, the so called cross-level interactions may be defined that allow for the

estimation not only of main effects at the individual and contextual levels,

but also of their interaction. It is this interaction between agentic and struc-

tural variables that most often motivates life course scholars.

A fourth, promising analytical approach is represented by exploratory

analyses of repeated measures data. These techniques are newer but are

quickly gaining popularity in life course research, thanks to their capacity to

address not only quantitatively but also qualitatively motivated questions.

Ritschard and Oris discuss Markov transition models and longitudinal data

mining. Both techniques are concerned with sequences of events that do not

need to follow specific assumptions. Mathematical rules can be established to

explain the probability of switching from one event to another and the effect

exerted by chosen covariates on these switches. While Markov models are

parametric, data mining is non-parametric and aims at deriving

association rules among the most typical sequences and their frequencies.

A typical practical application is that of online bookstores, where custo-

mers purchasing item A are notified of similar items bought by preceding

customers who also purchased item A. Another promising longitudinal ex-

ploratory technique is that of optimal matching (Abbott, 1995), of which Bird

and Kruger remind us in their contribution. This technique, unlike EHA,

is not focused on a specific event, but aims instead at producing typical

sequences, i.e., in the case of life courses, longitudinal constellations of states,


allowing to study whole trajectories. The assumptions of the states are min-

imal, so that their complexity may drive the synthesis (Widmer et al., 2003b).

Through pairwise comparisons this inductive method computes the minimal

distance separating each pair of individual trajectories available in the sample

according to pre-defined ‘‘costs’’ needed to transform one trajectory into

another. The resulting distance matrix may then be fed into a clustering

procedure to obtain groups as a function of their shared types of trajectories.

These four families of techniques6 have developed separately, with little or

no links between them. In recent years, however, methodological advances

have allowed to combine appealing features of these methods to benefit

further from their applications (Billari, Ritschard & Oris, and McArdle).

Examples include the combination of EHA and MLM (Ritschard & Oris)

that allow for conditioning the probability of the occurrence of an event on

information at different levels of the data organization. Similarly, the recent

addition of SEM latent variables in EHA refines the measurement properties

of predictors, so that their effects are less attenuated by unrelated variance

such as error. SEM and MLM have also recently been combined to provide

for another methodological synergy (Ghisletta & Lindenberger, 2004;

Rovine & Molenaar, 2000). Here, the advantages of SEM with respect to

measurement properties are joined with the power of MLM to disentangle

effects stemming from different, hierarchically organized sources of variance.

Methodological refinements have contributed much to the advancement

of not only empirical but also theoretical knowledge about the life course. At

the same time, the methodological and data-analytical techniques have ad-

vanced themselves, geared as they have become to address further theoretical

questions raised by life course scholars. The combination of now well-

established techniques and the quickly evolving field of linear and non-linear

dynamical systems will further contribute to paving the road of life course

research. We believe that a fundamental ingredient for successful life course

research is the presence of continued synergetic communication between the

different disciplines concerned. Good data-analytical tools have emerged in

each discipline, and we are confident that yet better tools will be developed

by combining discipline-specific existing techniques. These ameliorations are

instrumental to acquire deeper knowledge of life course phenomena.

INTERDISCIPLINARITY

After having sifted through the four sections of this volume with a view to

common or interacting themes between its contributions, let us take up the

RENE LEVY ET AL.380

three axes we propose for developing interdisciplinarity in life course

research, common concepts, theoretical bridges, and transversal themes –

what elements can this volume contribute to each of these?

We have to realize that common concepts and transversal themes are

closer to each other than both are to theoretical bridges, since we almost

inevitably use concepts to refer to themes. This allows us to treat both

aspects together. Common concepts have to develop from interdisciplinary

work on transversal themes – they need not be identical from the outset,

otherwise interdisciplinarity would be restricted to the rare instances where

the same concept is used in more than one discipline (examples could be

socialization or coping), and even if the words were not the same, inter-

disciplinary connections would boil down in such cases to simple termino-

logical translation (as in the case of ‘‘lifespan’’ and ‘‘life course,’’ or of the

different names used for some data-analytical methods). Conceptual com-

parison and elaboration become interesting if concepts are not the same

between two or more disciplines, but substantively close enough to favor

interdisciplinary exchange and elaboration, which is most likely to be fruit-

ful when starting from common themes.

Among the candidates for becoming common or transversal themes that

have emerged in this volume, let us single out socialization, age, identity

change and life course transitions, agency, coping, and gender. In varying

configurations, each of these substantive areas promises for interdisciplinary

collaboration to bring about mutual enrichment, greater strength

of analytical grip, and more complete understanding of life course phe-

nomena, especially if we start from the principle to look first at inter-

dependencies rather than causal chains because the latter would fix a priori

hierarchies of causation between the disciplines. Socialization is perhaps the

most traditionally common area, at least between psychology, social psy-

chology and sociology, because the three disciplines consider it to be one of

the most basic processes of personal development; the particular perspec-

tives developed by each of them is a crucial source of complementarity.

The situation is different for age, the analytical status of which is clearly

less obvious and more controversial. But then, interdisciplinary discussion

of this situation should lead to spell out more explicitly the discipline-

specific perspectives and assumptions, facilitating to arrive at a more en-

compassing and articulate conceptualization of the phenomena concerning

age and the related processes. As an illustration, let us elaborate somewhat

further on this theme. In psychology, the influential article by Wohlwill

(1970) pinpointed this problem. This author characterized the status of age

as a convenient, descriptive and data-organizing tool that lacked however


theoretical meaning. He urged scholars to investigate the aspects of behavior

that might be lawfully related to age. Among the few examples embracing

this deeper analysis of the variable of age we can cite the search for markers

of biological age (or biomarkers; e.g., Anstey, Lord, & Smith, 1996), the

opposition of chronological age to other time definitions that are theoret-

ically better justified in the light of the phenomenon under analysis (e.g.,

time left to the onset of pre-clinical dementia when studying memory per-

formance in older adults; Sliwinski, Hofer, Hall, & Buschke, 2003), and,

from a more analytical point of view, transformations of age in relation to

the variable considered in order to better understand the age-related mech-

anisms (e.g., McArdle, 1986). In much the same vein, sociologists Settersten

and Mayer (1997) have advised, ‘‘chronological age itself is an ‘empty’ var-

iable...it is whatever age presumably indexes that is thought to be impor-

tant.’’ The major difference between psychological and sociological uses of

age in the life course/life-span field is probably that in developmental psy-

chology, it indicates changes implied in forms of physical and cognitive

maturation and physical or physiological aging, i.e., biological age, whereas

sociologists think in terms of social age, again with several specific meanings

that are not always clearly distinguished, especially in the sense of age norms

versus the more structural sense of specific roles or role sets that define the

way individuals are embedded in the social world. In sum, life course schol-

ars more than others are urged to move the age variable from the right side

to the left side of the equation: age should not be used to explain behavior,

but should itself become subject to analysis. A further hint at a socio-

logical contribution to the substantive interpretation of age can be seen

in Kohli’s (1985) discovery of biographical chronologization as a rather

recent historical process, related to what Weber already analyzed as

the growing bureaucratization and rationalization of modern societies. Ac-

cording to Kohli, the sequencing and temporal ordering of modern life

courses that has taken place in the last 2–3 centuries is explained mainly by

the structural changes brought about by modernization, reliance on age for

the legal and procedural attribution of a series of rights and duties corre-

sponding to what Weber called bureaucratical rationality (among such in-

stitutional innovations, let us mention the implementation of compulsory

scolarization of all children along with the prohibition of children’s

employment, the fixation of a series of legal age thresholds, and various

welfare-state regulations with life course incidences, be they age related or

not). Seen from this vantage point, the more or less regular timing of some

crucial events in modern life courses, especially ‘‘normative transitions’’, no

longer appears as a definitional element of life courses as such, but as one of

RENE LEVY ET AL.382

the various ways in which they may be socially institutionalized and stand-

ardized. Facing this empirical situation, we turn out to be rather poor in

theoretical tools permitting to conceptualize such findings – take the pure

timing dimension away and see what remains in terms of life course an-

alytical tools! Not much, at least in the conceptual traditions we mentioned

up to now; we shall come back to this question with respect to conceptual

bridges.

For identity change and life course transitions, we have proposed some

hypotheses in an interdisciplinary perspective; this may be somewhat of a

test area for our postulate of a priori symmetry between the disciplines –

sociologists as well as demographers will certainly have a tendency to as-

sume that social regulations trigger identity changes rather than the other

way round, but a more agentic and also a (social-) psychological perspective

will want to consider with equal interest the possibilities of active individual

construction of transitions. Again, the various disciplines can only gain at

working together in this area because they have developed different and

potentially complementary conceptions of an issue they share, but that goes

beyond their specific horizon.

Let us pass more summarily on the remaining examples of transversal

themes. Agency as a theme is almost per definition a meeting place for

psychological, social–psychological and sociological aspects, including cog-

nitive, affective, cultural, behavioral and interactional dimensions; coping

may be less common a topic for demography and sociology than for the

other two disciplines considered here, but its importance has long been

acknowledged there, too. Finally, gender may need less insistence on the

fruitfulness of an interdisciplinary approach than any of the other themes,

but it should be underscored that probably few other perspectives than the

study of life courses are equally apt at highlighting processes of gendering,

and notably not only on the level of interindividual doing gender, but also

on an institutional level – provided, of course, research is done in a gender-

sensitive way (Eichler, 1988).

What about theoretical bridges? We may distinguish between two levels of

theorizing, a Merton-like level of middle-range theorizing that corresponds

to the concepts used in subject areas like those we have just mentioned, and

a more metatheoretical or abstract level of general conceptual thinking. One

track of interdisciplinary development on this second level is indicated by

the recent FFT and SFM models in social psychology mentioned above.

They provide explicit entry points for interactions with the person’s social

environment as conceptualized, e.g., by sociological approaches – an aspect

they share with Bronfenbrenner’s principle of ecological psychology or


Erikson’s opening for social aspects entering into the stage-defining dilem-

mas of epigenesis he postulates, but their advantage can be seen in their

firmer grounding in empirical research.

This theoretical ‘‘offer’’ from personality psychology may be seen as a

major anchor point for building conceptual bridges between psychology and

sociology – let us try to develop, as a counterpart, a sociological anchor

point, taking up the question of how and in which ways the link between

specific role sets and their corresponding ages is socially constructed, espe-

cially on the level of macro- and mesosocial institutions. There is a prom-

ising track in sociological thinking, indicated, for example, by Kerckhoff

(1993, p. 13) in a passage where he thinks about concepts for analyzing

professional careers: ‘‘...we need to chart the movementyof individuals over

time as they pass through a number of stages in the life course and occupy

positions within hierarchically structured social organizations. At each

stage, we need to be able to identify a set of locations that are hierarchically

ordered, and we need to measure the personal characteristics of the indi-

viduals occupying those locations. yCharting the flow of individuals be-

tween structural locations across stages in the life course constitutes

describing the ‘careers’ of those individuals, (i.e.,)ythe pathway (they fol-

low)ybetween positions in the social structure occupied at different points

in the life course.’’7

In a more general stance, we can define this structural part of the life

course as a movement through social space. Taking into account the basic

understanding that social space is generally organized in relatively well-

defined and well-delimited social fields (Lewin, 1935; Bourdieu, 1980) and

that – due to multiple participation – our social participation is generally

definable by status/role sets or profiles rather than by single statuses and

roles, we can reformulate this heuristic definition of the life course as a

sequence of participation profiles (Levy, 1977, 1991).8 The linking to age of

specific features of such sequences, especially of specific transitions between

subsequent participation profiles, for instance, on the basis of cultural age

norms (Neugarten, Moore, & Lowe, 1965; Settersten, 1997) some of which

may be more officially institutionalized (Kohli’s chronologization), is but

one of the multiple ways of life course standardization. The sequential or-

dering of a series of institutional participations (Kruger, 2001, Bird &

Kruger) is another one. It is, however, important to stress that life course

standardization is not a necessary ingredient of this analytical vision, it is

one of its variable dimensions. Analyzing life courses as sequences of par-

ticipation profiles, e.g., in a social structural perspective, enables us to more

fully characterize the mechanisms that relate biographically changing social

RENE LEVY ET AL.384

participation to ongoing time without allowing the social structural aspects

to go unobserved or to remain poorly defined (Settersten, 2005).

Turning back to articulating the sociological and psychological perspec-

tives, we may then ask to what extent psychological development and aging

(cognitive, emotional, moral, identity), besides its ontogenetic ‘‘push fac-

tors’’, can also be shown to develop according to a threshold rather than

gradual model that would, however, not express endogenous stages, but

materialize in relation with transitions in participation profiles, and more

specifically to what extent some changes of participation profiles, rarely

studied in sociology, could have a psychological impact, e.g., profile exten-

sions or restrictions in the sense that the number of simultaneous partici-

pations before and after a transition grows larger or smaller.9

Another substantive theoretical axis that may help articulate or integrate

conceptual contributions of different disciplines can be seen in the distinc-

tion of various system levels, once different usages of some terms are spelled

out and possibly agreed upon (e.g., what constitutes micro- and macrolevel

phenomena differs dramatically between psychologists and sociologists). It

would certainly be simplistic to assign to each discipline its proper system

level, thus restraining it to the level in question (as Devereux, 1967, once

proposed with his ‘‘complementaristic’’ conception of ethnopsychoanalysis).

One discipline can legitimately study several such levels, some of which are

also focused by other disciplines. This is especially evident for the subject

matters shared by social psychology and psychology on the one hand, and

sociology and social psychology on the other, but also and probably even

more so of sociology and social demography. In this perspective, Settersten

and Gannon’s formulation of agency within structure is not only a formula

to overcome the artificial opposition of these complementary aspects of

social reality, but to integrate more generally individual- and social-level

explanations. Even if the classical distinction between structure and agency

may have a singular ideological and political interest, it can be seen as a

special case of this more general dimension of system-level differentiation.

Spelling out and studying the mechanisms that relate different system levels

with each other remains one of the lesser studied and poorly conceptualized

areas in all concerned disciplines, and calls intrinsically for inter-

disciplinarity (Diewald, 2001). Saying this, we should also note that this

volume (and probably most similar ones) does not cover all the possible

system types and levels that are relevant for life course research. The social

sciences certainly have to develop more explicit and integrative theoretical

and methodological interfaces toward the biological system and its processes

of ontogenesis on one hand (Shanahan, Hofer, & Shanahan, 2003) and, on


the other, toward historical and cultural processes as well as actual processes

of institutional framing that intervene heavily on the life course (see Elder,

1974; Heinz, 1992; Kruger & Levy, 2001).

Let us end this volume with a very general statement about inter-

disciplinarity. Once we are convinced of interdisciplinary work as a superior

way to study life courses, we have to define the ways we want to ‘‘mix’’ the

conceptual tools our various disciplines can bring to bear on the topic. One

basic principle has already been stated, it is what we could call the principle

of a priori disciplinary symmetry. A second basic principle should inform

our attempts, we could call it an articulation principle: the various disci-

plines cannot and should not be simply melted into some unifying theo-

retical mold, given that they focus on complementary aspects of the same

phenomena as well as on different and complementary levels of the systemic

organization of reality. For that reason, to develop its potential, their sci-

entific synergy needs explicit articulations, not fusion, and even less con-

ceptual vagueness. The way leading to this goal is certainly difficult and

laborious; we hope that this volume is more dynamic than the proverbial

signpost that points to a direction without going there itself, i.e., that we

have not only been able to point out some promising tracks, but also to

undertake some real steps in that direction.

NOTES

1. Throughout this final chapter, all references to contributing authors concerntheir contributions to this volume.2. Let us recall Marshall’s distinction between the principle of agency as an in-

gredient of human nature and the practical competence and possibility to act as avariable characteristic. Freire’s (1972) concept of conscientization can be cited asaiming at empowering oppressed individuals in order to give them the practicalcapacity to actualize this latent part of their being humans.3. ‘‘Critical’’ does not necessarily mean threatening and even less unexpected – the

following considerations are not limited to ‘‘non-normative’’ transitions or events.What is implied here is simply the idea that compared to the more or less stableperiods between transitions, the latter tend to entail to a larger extent processes ofchange and adaptation on various levels. It goes without saying that this postulate isnot meant to exclude the possibility of important processes and consequences at-tributable to what goes on during the phases between transitions.4. We use this term in a rather general sense, much as it was already used by

Lewin (1951) in his field theory approach where he defined a field as the totality ofsimultaneous and interdependent features that form a situation; however, we wouldinsist more than he did on the systemic character of social fields (see also Bourdieu,1984).

RENE LEVY ET AL.386

5. Two methodological problems that may be avoided by properly applyingMLMs are the atomistic and the ecological fallacies, which incorrectly assign groupeffects at the individual level or, vice versa, individual effects at the group level,respectively.6. A fifth, particularly promising set of techniques that life course scholars have an

interest to adopt is that of linear and non-linear dynamical systems (e.g., Kaplan &Glass, 1995; in this volume, it has only been alluded to by McArdle). In this large setof techniques, the outcome of interest is not the value of a variable at a given timepoint, but the change in that variable over time. These methods presuppose that thehuman organism can be conceived as an open living system, continuously fluctu-ating, achieving a dynamic equilibrium through self-organizing structures tendingtowards homeostasis (not limited to its physiological meaning). These techniquesprovide a way to characterize many of the basic phenomena of development, in-cluding change, stability, variability, stages, continuity, and the combination ofquantitative and qualitative change as well as the emergence of new forms of struc-ture and function.7. Similar ideas can be found in the thinking of Rosow (1976).8. Without neglecting the fact that participation in a social field implies also

holding a position in the field’s internal structure and assuming the correspond role.All these aspects have multiple implications that may be of importance when anal-yzing life course sequences, but cannot be spelled out here.9. Among the rare studies of this aspect, let us cite Thoits (1986) and Moen,

Dempster-McCain, & Williams (1989, 1992) who show beneficial effects of multiplerole occupancy on women’s health. In this perspective, retirement corresponds to amajor profile-narrowing.

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392

AUTHOR INDEX

Abbott, A. 13, 188, 274, 309, 364, 379

Abelson, R. 238

Abrams, D. 202

Ackerman, S. 62

Adler, J. 247

Agrawal, R. 301

Ahrons, C. 160

Aisenbery, S. 188

Alea, N. 219–220

Alexander, K.L. 132

Allison, P.D. 266, 289

Allport, G.W. 239, 371

Alpert, A. 333

Alter, G. 287, 291–293

Alwin, D.F. 37, 69, 85, 373, 375–376

Ambady, G. 241

Anderson, E. 317, 326, 351

Andres, D. 226

Anstey, K.J. 382

Anyadike-Danes, M. 277

Anyidoho, N.A. 245

Appice, A. 303

Aranza-Ordaz, F.J. 289

Arbuckle, J.L. 351

Arbuckle, T.Y. 226

Archer, M.S. 70, 85–86

Arnett, J.J. 243–244

Attanucci, J. 14

Attias-Donfut, C. 95

Axinn, W.G. 266, 293

Ayers, M.S. 220

Bakan, D. 62, 246

Baltes, P.B. 11–12, 14, 28, 43, 48, 132,

221, 231, 241, 315–317, 361, 378

Bandura, A. 132

Barber, B.L. 168

Barber, J.S. 266, 293

Bartlett, F.C. 219–220, 229

Barutchu, A. 202

Bates, D.M. 351

Bauer, J.J. 245

Bauman, Z. 99

Baumeister, R.F. 239

Bawin-Legros, B. 95

Buchel, F. 190

Beck, U. 42, 174, 179

Becker, H.S. 156

Becker, S.O. 271–272

Becker-Schmidt, R. 176–177

Beck-Gernsheim, E. 179

Beckmann, P. 180, 190

Beekink, E. 291–292

Begin, J. 107

Bell, R.Q. 317, 333, 351

Belli, R. 376

Belsky, J. 123

Bengtson, V.L. 95

Berchtold, A. 295–296, 299

Berger, P.L. 69–70, 73, 363

Bernard, H.R. 206

Berntsen, D. 219

Berquo, E. 286

Bickel, J.-F. 96

Billari, F.C. 261–262, 273–274, 287,

299, 301–303

Billig, M.G. 202

Bird, K. 180–182

Birkett, H. 342

Blakeslee, S. 159

393

Blechler, M. 46

Bleuler, E. 97

Block, J. 342

Blockeel, H. 299, 301–302

Blossfeld, H.-P. 174, 177, 187,

189, 265–268, 278, 287–288,

290, 296

Bluck, S. 219–221, 243, 250

Bock, R.D. 350

Bocquier, P. 266, 287

Bogen, S. 122

Boker, S.M. 348, 351

Born, C. 179, 182, 189, 368

Bouchard, T.J. 240

Bourdieu, P. 300, 384, 386

Bower, J.E. 374

Bowman, P.J. 246–247

Bradway, K.P. 318, 320, 348

Brainerd, C.J. 220

Brand, C. 109

Brandtstadter, J. 40, 43, 132, 372

Brannen, J. 102, 108

Breiman, L. 26, 263, 300,

303, 306

Brewer, W.F. 220

Bronfenbrenner, U. 28, 38

Brooks-Gunn, J. 164

Brostrom, G. 291–293

Brown, C. 245

Brown, J.D. 374

Brown, R. 200

Browne, M. 351

Browne, W. 293

Bruner, J.S. 238, 242, 252

Bryk, A.S. 293, 328, 335, 351

Bryson, A. 270

Buckholdt, D.R. 85

Bundy, R.F. 202

Burkhardt, A. 109

Buschke, H. 382

Busch-Rossnagel, N.A. 60, 84

Butler, R.N. 82, 230

Bynner, J. 210

Cain, L.D. 72

Call, K.T. 135, 144

Camburn, D. 376

Campbell, A.A. 157, 364

Campbell, C. 68–69

Campbell, D.T. 345

Campbell, R.T. 85

Cantor, N. 241

Carstensen, L.L. 47

Caselli, G. 285

Casey, K. 238

Cattell, R.B. 316, 338, 342

Cavanaugh, J.C. 226

Ceci, M. 303

Chaikelson, J. 226

Charme, S. 239, 243

Chartrand, E. 107

Cherlin, A.J. 110, 159

Christensen, K. 271

Chryssochoou, X. 374

Ciampi, A. 303, 309

Cinnirella, M. 17

Cinotta, S. 202

Clarke, P. 69

Clausen, J.A. 40, 132, 135, 156, 168

Clausen, J.S. 68, 80, 218

Clogg, C. 144

Cnaan, A. 324

Coenen-Huther, J. 106, 111

Cohler, B.J. 99, 109, 114, 238, 244

Colby, A. 199, 248

Coleman, J.S. 138

Collins, R. 85

Colvin, C.R. 240

Congdon, R. 293

Conger, R.D. 123

Connidis, I.A. 106, 121

Converse, P. 209

Conway, M.A. 219–220, 238

Cook, T.D. 44, 345

Costa, P.T. 224, 237, 239–240, 252, 371

Counts, D. 73

Courage, M.L. 242

AUTHOR INDEX394

Courgeau, D. 12, 19, 266, 287, 289

Cox, D.R. 264, 289

Crawford, J.R. 324

Cremer, C. 190

Criqui, M.H. 240

Crompton, R. 189

Crosnoe, R. 173, 187

Crowley, J. 309

Cudeck, R. 329, 333, 351

Cumming, E. 73

Curran, P. 351

Currie, D. 368

Czikszentmihalyi, M. 204

Dahrendorf, R. 69

Damon, W. 248

Dannefer, D. 41, 47, 59, 64, 85, 237

Day, R. 246

De Bruijn, B.J. 14

de Coninck, F. 19

De Rose, A. 303

de St. Aubin, E. 241, 245, 247–249

Deary, I.J. 239–240, 324

Dehaspe, L. 301

Dehejia, R.H. 269

Del Vecchio, W. 240

Demeny, P. 285

Dempster-McClain, D. 387

Denzin, N. 250

Devereux, G. 385

Diamond, A. 247, 249

Diehl, M. 60

Diewald, M. 40, 42, 385

Diggle, P.J. 341

Dilthey, W. 81

Dixon, R.A. 227

Doise, W. 17, 201

Donaldson, G. 316–317

Donati, P. 95

Dorsett, R. 270

Doyle, G.C. 218

Draper, D. 293

Drobnic, S. 177, 189

du Toit, S.H.C. 329, 333, 351

Duncan, L.E. 241

Duncan, S.C. 333

Duncan, T.E. 333

Dupaquier, J. 284

Dupaquier, M. 284

Durante, M. 202

Durkheim, E. 42

Durkin, K. 16

Dykstra, P.A. 262, 287

Eccles, J.S. 44, 168

Edel, L. 239

Edelstein, W. 14

Edmunds, J. 100

Eerola, M. 266

Ehmer, J. 118

Ehrsam, R. 222

Eich, E. 227

Eichler, M. 383

Eisenstadt, S. 362

Elder, G.H.Jr. 6, 12, 17, 44, 60, 63,

65–66, 68, 72, 106, 123, 127–128,

134, 155–156, 173–174, 187, 189, 218,

232, 237, 244, 252, 273, 361, 363–364,

368, 375, 386

Eliason, S.R. 144, 151

Ely, M. 238

Emler, N. 14, 197, 203, 205–206,

209–210, 212

Emmons, R.A. 241

Engelbrech, G. 190

Engfer, A. 369

Engstler, H. 111, 124

Entwisle, D.R. 132

Epstein, D.B. 317, 326, 329, 335,

338, 351

Epston, M. 251

Erikson, E.H. 14, 16, 82, 198,

217, 230, 237, 239, 241,

243, 371

Erzberger, C. 23, 183, 188

Esping-Andersen, G. 49

Author Index 395

Esser, H. 190

Essex, M.J. 16

Fahrenberg, J. 223, 226

Farkas, J.I. 174

Ferrer, E. 351

Ferrer-Caja, E. 331

Filipp, S.-H. 106–107

Finch, J. 95

Finch, M.D. 151

Fingerman, K.L. 108

Fitzgerald, J.M. 225, 229

Flaherty, B.P. 139

Flament, C. 202

Flanagan, C.A. 46

Flavell, J.H. 198

Foley, J.M. 247

Foner, A. 155

Forrest, J.D. 29, 309

Foster, E.M. 46

Frank, E. 303

Frankel, M.R. 293

Freedman, D. 376

Freire, P. 386

Freud, S. 98, 217

Freund, A.M. 40, 132, 241

Friedman, H.S. 240

Friedman, J.H. 303, 306

Friis, H. 95

Furnkranz, J. 274, 299, 301–303

Fthenakis, W.E. 369

Funder, D.C. 240

Furstenberg, F.F. 44, 46, 155, 157–159,

164

Gabriel, Y. 238

Gallagher, M. 159

Gannon, L. 35

Gardiner, J.M. 219

Garrett, M. 202

Gauthier, A. 95

Gauthier, J.-A. 366, 369, 380

Gecas, V. 47, 132

George, L.K. 11, 62, 69, 86

Gergen, K.J. 238, 244

Ghisletta, P. 361, 380

Giarusso, R. 95

Gibbons, R. 351

Giddens, A. 61, 65, 70, 85,

174, 241, 244

Giele, J. 40

Gifford, E.J. 46

Gigerenzer, G. 41

Giles, H. 201

Gilligan, C. 14

Gjerde, P.F. 251

Glass, L. 387

Godard, F. 19

Goffman, I. 75, 81, 112

Gold, D.P. 226

Goldberg, L.R. 240

Goldman, B.M. 366

Goldstein, H. 293, 351, 379

Goode, W. 213

Goody, J. 86

Gosling, S.D. 371

Gottschall, K. 182

Grabowski, L.J. 135, 144

Grambsch, P.M. 290, 293

Green, E.E. 226

Greenhalgh, S. 285

Gregg, G. 239, 250

Griffin, D.W. 105

Gruenewald, T.L. 374

Grunebaum, H.U. 99, 114

Gubrium, J.F. 85, 238

Gutmann, D. 61, 244

Gutschner, P. 118

Habermas, T. 243, 250

Haely, M. 293

Hagestad, G.O. 16, 47, 156, 174, 287

Hakim, C. 177

Hall, C. 382

Hall, M. 303

Hallahan, M. 241

AUTHOR INDEX396

Hamagami, F. 318, 320–321, 329,

331, 333–335, 337–338, 345,

347–349, 351

Hamerle, A. 266, 288

Hammer, R. 366, 369, 380

Hampel, R. 223, 226

Han, J. 300

Han, S.-K. 186, 189, 366

Hand, D.J. 300

Hankiss, A. 243

Harlow, S.D. 315, 352

Harootyan, R.A. 95

Harris, F. 189

Hashtroudi, S. 219

Havighurst, R.H. 16, 371

Hay, E. 108

Hayes, C. D. 158

Heckhausen, J. 43, 132, 372

Heckman, J. 270

Hedecker, D. 351

Heidrich, S.M. 218

Heil, F.E. 372

Heilbrun, C. 250

Heinz, W.R. 64, 85, 173–174, 185, 187

Hennessy, J. 202

Henry, W.E. 73

Herman, M.R. 44

Hermans, H.J.M. 239, 246

Hershberg, T. 157

Hertzog, C. 227

Hetherington, E.M. 168

Hewstone, M. 201

Hohn, C. 286

Hobcraft, J. 262, 266

Hoch, H. 110, 125

Hofer, D. 226

Hofer, S.M. 382, 385

Hoffman, B.J. 246

Hogan, D.P. 16, 174

Hogdson, D. 285

Hogg, S.A. 303, 309

Holford, T.R. 289

Holland, P.W. 270

Holman, T.B. 107

Holmes, J. 238

Holstein, J.A. 85, 238

Hooimeijer, P. 286

Hooker, K. 239, 241, 244,

371–372

Horn, J.L. 316–317

Hotz, V.J. 269–271

House, J.S. 133

Howe, M.L. 242

Hopflinger, F. 222, 224

Hradil, S. 179

Huang, Y.T. 245

Hughes, E.C. 72

Huinink, J. 183

Hultsch, D.F. 227

Ichino, A. 271–272

James, A. 40, 45

Java, R.I. 219

Jekeli, I. 106

Jenks, C. 40, 45

Jepperson, R.L. 59, 65–66

John, O.P. 240–241, 371

Johnson, K.M. 60, 63, 173, 187

Johnson, M.J. 107, 155

Johnson, M.K. 219

Jolesz, F. 321

Jones, G. 211

Jones, K. 321

Jones, R.L. 86

Josselson, R. 225

Joreskog, K.G. 338

Julien, D. 107

Junn, J. 209

Kahana, B. 61

Kahana, E. 61

Kahn, R. 48, 202

Kaiser, A. 222, 224

Kalicki, B. 369

Kalish, R.A. 75–76

Author Index 397

Kamber, M. 300

Kangas, J. 320

Kaplan, B. 245

Kaplan, D. 387

Kass, G.V. 304

Kastenbaum, R. 85

Katz, D. 202

Keane, M.R. 273

Kellerhals, J. 106, 366, 369

Kelly, J. 168

Kemeny, M.E. 374

Kennedy, S. 46

Kenny, D.A. 205

Kenrick, D.T. 205, 240

Kenward, M.G. 341

Kerckhoff, A.C. 384

Kernis, M.H. 366

Keyes, C.L.M. 219, 230

Kikinis, R. 321

Killworth, P.D. 206

King, L. 245

Kiser, E. 65

Kish, L. 293

Kissel, E. 247

Klein, D. 104

Klein, V. 180

Klineberg, J. 204

Kling, K.C. 16

Kling, V. 226

Klohnen, E.C. 241

Kluckhohn, C. 239

Kluge, S. 188

Knapp, G.-A. 177

Knellessen, O. 122

Knox, W.E. 135

Kohlberg, L. 198–199

Kohler, H.-P. 271

Kohli, M. 12, 187, 243, 363, 367, 382

Krampen, G. 372

Krebs, E. 222

Kruger, H. 50, 134, 150, 173–174,

176, 179, 182, 189, 363, 368,

384, 386

Kruglanski, A.W. 14

Kottig, M. 29

Kuh, D. 218

Kuijsten, A. 286

Kurtz, B. 180

Labouvie-Vief, G. 14

Laird, N.M. 324, 334, 351

Lalive d’Epinay, C. 96, 125

Landwehr, N. 303

Lang, F.R. 47, 102, 115

Langbaum, J. 239

Larson, R. 204

Lash, S. 174

Lautmann, R. 110

Lawton, M.P. 61

Layder, D. 69

Le Goff, J.-M. 111, 268, 361

Leary, T. 101

Leblanc, M. 309

Lee, J.C. 131

Lee, S. 97, 125

Lelievre, E. 12, 19, 266, 287, 289

Lemmon, H. 324

Lerner, R.M. 60, 84

Lettke, F. 102, 104, 106–107, 122

Leveille, S. 107

Levine, D.I. 270–271

Levinson, D.J. 237, 244, 252, 368, 371

Levy, R. 3, 11, 50, 175, 361, 363, 366,

369, 380, 384, 386

Lewin, K. 384, 386

Lewis, M. 246–247

Li, A. 50

Li, F. 333

Li, P.S. 368

Li, S.-C. 48

Lianos, G. 202

Lieberson, S. 160

Liefbroer, A.C. 291–292

Lillard, L.A. 267, 278, 290

Lim, K.O. 345

Lin, D.Y. 293

AUTHOR INDEX398

Lindenberger, U. 14, 28, 43, 221, 315,

361, 380

Lindsay, D.S. 219

Lindsay, P. 135

Littell, R.C. 351

Little, B.R. 241

Little, R.J.A. 324, 341

Little, R.T.A. 351

Littwak, E. 95, 125

Loevinger, J. 198

Loftus, E.F. 220

Logan, R.L. 121, 241

Lord, S.R. 382

Lorence, J. 135

Lorenz-Mayer, D. 179, 182, 189

Lorenz-Meyer, D. 107, 116, 368

Lowe, J.C. 17, 155–156, 384

Lowenstein, A. 117

Luscher, K. 93, 95–96, 102, 106–107,

110–111, 115, 118, 122

Luckmann, T. 69–70, 73, 363

Ludewig-Kedmi, R. 113

Lykken, D.T. 240

Lynch, K.A. 295

Mabry, B. 95, 123

Macaulay, D. 227

MacDonald, I.L. 295

Machado, M.A. 245

MacIntyre, A. 238

Maclean, M. 218

Macmillan, R. 144, 151

Maes, H.H. 351

Magnusson, D. 342

Malerba, D. 303

Malo, M.A. 309

Mandler, J.M. 242

Mannila, H. 300–301

Mansfield, E.D. 246–247, 249

Manting, D. 18

Manton, K.G. 291

Maples, J. 266, 293

Marcia, J. 198

Marini, M. 156

Markman, H.J. 107

Markus, H.R. 80, 132

Marshall, V.W. 57, 66, 69–70,

72–74, 78, 81–83, 85–86, 95,

173, 185

Maruna, S. 238

Marx-Ferree, M. 189

Mason, J. 95

Matthews, G. 239–240

Matthews, S.H. 95

Mayer, A.-K. 106–107

Mayer, K.U. 7, 12, 16, 42, 49, 174, 177,

181, 183, 187, 189, 266–267, 273, 288,

363, 370, 382

Mazzuco, S. 273

McAdams, D.P. 14, 17, 121, 126, 217,

230, 233, 237–239, 241, 243–250, 252,

371–372

McArdle, J.J. 315, 317–318, 320–321,

324, 326, 329, 331, 333–335, 337–338,

341, 344–345, 347–349, 351–352,

382

McCrae, R.R. 224, 237, 239–240,

252, 371

McCullagh, P. 310

McGue, M. 240

McKinney, S. 303, 309

McLanahan, S. 159

McLoyd, V. 46

McMullin, J.A. 69–70, 121, 124

McNamara, S. 206

McNicoll, G. 285

McVicar, D. 277

Mead, G.H. 100, 119

Meier, B. 226

Mercer, R.T. 218

Meredith, W. 318, 320, 328, 348,

350–351

Merton, R.K. 156

Metha, P.D. 351

Meyer, J.W. 59, 65–66

Meyerhoff, B.G. 86

Author Index 399

Miech, R.A. 68, 134

Milardo, R. 107

Milgram, S. 204

Milhoj, P. 95

Miliken, G.A. 334, 351

Mills, C.W. 49

Mills, M. 268

Milofsky, E. 241

Mischel, W. 240–241, 371, 373

Mitchell, J.C. 202

Mitteness, L.S. 114

Muller, W. 187, 363

Modell, J. 157

Moen, P. 174, 176, 186, 189,

366, 387

Mojardin, A.H. 220

Molenaar, P.C.M. 16, 380

Monopoli, M. 303

Moore, J.W. 17, 155–156, 384

Morgan, S.P. 164

Mortimer, J.T. 131, 133–136, 138–140,

144, 150–151, 174–175

Moskowitz, D.S. 62

Mounoud, P. 16

Mouw, T. 274, 276–277

Mueller, M.M. 66, 83, 173

Mugny, G. 17

Mullen, B. 230

Mullin, C.H. 269–271

Munoz, F. 309

Murphy, M. 262, 266

Murphy, S.A. 266, 293

Murray, H.A. 239

Murray, S.L. 238

Muthen, B.O. 342, 351

Muthen, L.K. 342, 351

Myrdal, A. 180

Nagin, D. 342

Neale, M.C. 351

Nelder, J.A. 310

Nesselroade, J.R. 316–317, 321, 333,

344–345, 352, 366, 378

Neugarten, B.L. 16–17, 155–156, 174,

371, 384

Nichols, E.G. 218, 233

Nie, N.H. 209

Noam, G. 14

Nurius, P. 80, 132

Nurmi, J.-E. 376

Nydegger, C.N. 114

Oakes, J.M. 39

Oesterle, S. 138, 140, 174

Ogg, J. 117

Olshen, R.A. 303, 306

Olson, L.S. 132

Ondrich, J. 190

O’Rand, A. 63, 65–66, 85

O’Rand, A.M. 174

Oris, M. 283, 287, 291–293, 299

Otscheret, E. 122

Painter, G. 270–271

Pajung-Bilger, B. 96, 102

Paliwal, K.K. 295

Pallara, A. 303

Panis, C.W.A. 267, 278

Parker, R. 111

Parsons, T. 95, 156

Pascual-Leone, J. 14

Passel, J.S. 39

Patten, A. 246–247

Peitz, G. 369

Perren, S. 225

Perrig, W.J. 217, 222, 226–227

Perrig-Chiello, P. 217, 222–227,

233

Petersen, T. 266

Peterson, C. 247

Pfefferman, A. 345

Phillips, M. 44

Philo, C. 45

Piaget, J. 198

Piccarreta, R. 274

Pillemer, K. 95–96, 106, 112, 118

AUTHOR INDEX400

Pinheiro, J.C. 351

Plakans, A. 118

Platow, M. 202

Plewis, I. 293

Pleydell-Pearce, C.W. 238

Polkinghorne, D. 238

Pollien, A. 366, 369, 380

Pomerantz, E. 237, 240

Pothoff, R.F. 350

Potter, J. 371

Poulain, M. 287

Powesland, P.F. 201

Prein, G. 23

Prescott, C.A. 337, 351

Pressat, R. 17

Pross, H. 180

Prout, A. 40, 45, 49–50

Prskawetz, A. 274, 299, 301–303

Potter, U. 278, 290

Purdon, S. 270

Pyke, K.D. 123

Quinlan, J.R. 303, 306

Rabiner, L.R. 295

Raftery, A.E. 295

Rakotomalala, R. 310

Ramon, J. 301

Ramsey, C.M. 245

Rao, C.R. 328

Rasbash, J. 293

Raudenbush, S.W. 293, 328,

335, 351

Reder, L.M. 220, 231

Reed, G.M. 374

Reicher, S.D. 202, 205

Rein, M. 95

Reinharz, S. 96

Reyna, V.F. 220

Reynolds, J. 246–247

Richards, L.N. 95

Ricoeur, P. 239, 242

Riegel, K.R. 14, 84

Riley, J.W. 37

Riley, M.W. 37, 72, 155

Rindfuss, R.R. 16, 87, 155, 174, 262

Ritschard, G. 283, 299, 303, 307

Robb, R. 270

Roberts, B.W. 237, 240

Roberts, K. 134

Roberts, R.E.L. 95, 127

Robertson, E.B. 123, 127

Rodgers, B. 218

Rogosa, D. 317, 350

Rohwer, G. 265–267, 278, 287,

290, 296

Romney, D. 210

Rosenbaum, P.R. 271–272

Rosenbloom, M.J. 345

Rosenfeld, R.A. 16, 87, 155, 174

Rosenthal, C.J. 95

Rosenthal, G. 29

Rosenthal, R. 241

Rosenwald, G. 250

Rosow, I. 387

Ross, K. 46

Rossi, A.S. 62, 95

Rossi, P.H. 39, 95

Rossier, C. 111

Rost, H. 190

Rothermund, K. 43

Rotter, J.B. 132

Rovine, M.J. 380

Rowe, J. 48

Roy, S.N. 350

Rubin, D.B. 271–272, 351

Rubin, D.C. 219–220

Rubin, M. 201

Ruch, M. 226

Rudorf, S. 109

Ruetzel, K. 247

Rumbaut, R. 46

Rusca, E. 231

Rutter, M. 217–218

Rybash, J.M. 225

Ryczkowska, G. 303

Author Index 401

Ryder, N.B. 156, 376

Ryff, C.D. 16, 69, 80, 218–219, 230

Ryu, S. 151

Sackmann, R. 175

Salmela-Aro, K. 376

Sameroff, A. 44

Sandefur, G. 159

Sanders, S.G. 269–271

Sarbin, T. 238

Sauvain-Dugerdil, C. 111

Sayer, A.G. 345, 351

Schacter, D.L. 219, 230

Schaie, K.W. 12

Schank, R. 238

Schmidt, B. 177

Schneider, N.F. 190

Schoeni, R. 46

Schoepflin, U. 12, 363

Schutze, Y. 181

Schulz, R. 372

Schutz, A. 70

Schwartz, J.E. 240

Schwartz, S.H. 241

Schwartzman, A. 226

Schwarz, N. 219

Scollon, C.K. 245

Scott, J. 373, 375

Seber, G.A.F. 329

Segal, M.R. 309

Segal, N.L. 240

Selg, H. 223, 226

Seligman, M.E.P. 247

Serafin, D. 226

Settersten, R.A. 4–5, 16, 28, 35, 39–40,

42–44, 46, 48, 52, 60, 69, 156, 174, 267,

273, 370, 373, 382, 384–385

Sewell, W.H. 37, 70, 85

Shanahan, L. 385

Shanahan, M.J. 50, 68, 133–134, 139,

151, 385

Shanas, E. 95

Sheldon, K.M. 239

Shifley-Grove, S.S. 225, 229

Shoda, Y. 241, 371

Shuey, K.M. 106

Sibeon, R. 43

Silverstein, M. 95

Singer, J.A. 241, 245–246

Singer, J.D. 351

Skevington, S. 202

Skinner, M.L. 123

Skytthe, A. 271

Slasor, P. 324

Slater, P.E. 208

Sliwinski, M.J. 382

Small, B.J. 227

Smelser, N.J. 70, 99, 113, 122

Smith, G.A. 382

Smith, J. 40

Smyth, P. 300

Spangler, D. 106

SpieX, K. 190

Spini, D. 361

Spiro, A. 321

Sorbom, D. 338

Sørensen, A. 174

Srikant, R. 301

Srivastava, S. 240, 371

Staff, J. 131, 138, 140

Stallard, E. 291

Starr, J.M. 324

Staudinger, U. M. 14, 28,

43, 100, 120,

221, 315, 361

Stehlik-Barry, K. 209

Stehouwer, J. 95

Stewart, A.J. 241

Stahelin, H.B. 222, 226

Stone, C.J. 303, 306

Stoup, W.W. 334, 351

Strack, F. 219, 234

Strassen, J.-F. 95

Strauss, A.L. 156

Stringfield, D.O. 205

Struyf, J. 301

AUTHOR INDEX402

Strycker, L.A. 333

Sturzenegger, M. 222, 224

Stuss, D.T. 219

Sugarman, L. 218

Suitor, J.J. 106, 112

Sullivan, E.V. 345

Suls, J. 230, 234

Sussman, M.B. 95

Swicegood, C.G. 16, 87,

155, 174

Szreter, S. 285

Szydlik, M. 95

Tajfel, H. 200, 202–203

Tashakkori, A. 23

Tavare, S. 295

Taylor, S.E. 374

Teddlie, C. 23

Tellegen, A. 240

Therneau, T.M. 290, 293

Thiffault, J. 303, 309

Thoits, P.A. 387

Thom, R. 16

Thomas, W.I. 63

Thompson, C.W. 320

Thompson, M.M. 105

Thorne, A. 244–245

Thornton, A. 376

Thurlemann, F. 122

Tisak, J. 328, 350–351

Tolke, A. 183

Toivonen, H. 301

Tomasello, M. 242

Tomlinson-Keasy, C. 240

Tostain, M. 14

Townsend, P. 95

Tsay, A. 274

Tucker, J.S. 240

Tucker, L.R. 328

Tulving, E. 219

Turiel, E. 14

Turner, B.S. 100

Turner, J.C. 201, 203

Uhlenberg, P. 59

Vaillant, G.E. 218, 241

Vallin, J. 284–285

Van de Kaa, D.J. 285

Van de Water, D. 247

van der Heijden, P.G.M. 274

van der Maas, H.L. 16

Van Imhoff, E. 286

van Poppel, F. 291–292

van Wissen, L.J.G. 262, 286–287

Vaupel, J.W. 291

Verkamo, A.I. 301

Viaud, J. 11, 16

von Allmen, M. 106

von Matt, P. 97

Veron, J. 285

Wadsworth, M.E.J. 218

Wahba, S. 269

Waite, L. 159

Wallace, C. 211

Wallerstein, J.S. 159

Walshe, J. 202

Ware, J.H. 334, 351

Weber, M. 64, 69

Weddeburn, D. 95

Wei, L.J. 293

Wellman, H.M. 242

Wertlieb, D. 218

West, M.A. 202

West, S.G. 351

Whalley, L.J. 324

Wheeler, M.A. 219

White, M. 251

Whiteman, M.C. 239–240

Whittlesea, B.W.A. 220

Widmer, E. 361, 366, 369, 380

Wiese, B.S. 132

Wild, C.J. 329

Wilensky, H. 157

Willekens, F.J. 284

Willett, J.B. 317, 333, 345, 350–351

Author Index 403

Williams, N. 202

Williams, R.M. 387

Williams, T. 245

Willms-Herget, A. 179

Willson, A.E. 106

Wingard, D.L. 240

Wingens, M. 175

Winsborough, H. 156

Winter, D.G. 241

Wishart, J. 328, 331

Wohlwill, J.F. 5, 381

Woike, B. 239, 245–246

Wolfinger, R.D. 334, 351

Wolpin, K.I. 273

Woodcock, J.R. 326, 331, 351

Woodcock, R.W. 331

Woodhouse, G. 293

Wothke, W. 351

Wright, R. 220

Wu, L. 50

Wu, L.L. 188, 266–267, 274

Wunsch, G. 285

Wurzbacher, G. 180

Xenos, P. 286

Xie, G. 351

Yamaguchi, K. 266, 289

Yang, M. 293

Yang, Q. 190

Young-DeMarco, L. 376

Zanna, M.P. 105

Zeger, S.L. 352

Zighed, D.A. 303, 307, 310

Zima, P.V. 105

Znaniecki, F. 63

Zucchini, W. 295

Zuroff, D.C. 62

AUTHOR INDEX404

SUBJECT INDEX

accelerated failure time model 289

ACT effects 316–317

action-structure problem 69–70

adaptation 61

adjacent institutionalization 51

adolescence 45, 144, 146, 248

affection 107

agency 39–41, 44, 49, 58–69, 83,

144–149, 246, 362–365

agency and structure 21–22, 35, 71–83,

131, 364–365, 370, 379

agency within structure 41–43, 51–52,

385

agentic decision-making and behavior

133

agentic strategies 133

aging and dying 58, 71–72

AIC criteria 298, 307, 308

algorithmic culture 274–278

ambivalence 93, 95, 97–105,

110–119

ambivalence diversification 108–110

ambivalence understanding elements 98

analysis of covariance 334–335

assessment and differentiation of

ambivalence 105–108

assessment of relationships 105–108, 112

attitude 220, 230

autobiographical coherence 243

autobiographical memory 24, 219–220

autobiographical reflection selectivity

81

autobiographical self 242

average effect of treatment 270–271

awareness and timeframes 186

baby break 178–182

Bartlett’s schema 220

basic tendencies 371–372

BIC criteria 298, 307–308

Big Five traits 240, 371

biographical experiences 217–218

biographical memories 225–227

biographical recollection 227–230

biographical reconstruction 23–25, 195,

370–374

biographical transition 217–218, 222,

224

Bradway–McArdle collection 335

Bradway–McArdle Longitudinal Study

318, 320–321

Captivation 104

Caring 113–117

causal approach 265–269

causal coherence 243

causal impacts of events 269–273

causal inference 270

‘causality’ culture 265–273

challenges and contradictions of life

course 35

changes in identity 81–83

changing social identity 208–209

characteristic adaptations 241, 371–372

child-rearing leave 178–180

childhood and adolescence 44–45

childhood friends and playmates 208

class-theoretical models 38

classification tree 303, 309

cognitive functioning 198–199, 226

405

cognitive growth for males and females

340–342

cognitive skills 243

communion 62, 246

conceptualizing ambivalence 97–104

constructivist views 40

contamination sequences 246–247

contemporary discourse reframing

186–187

continuous-time model 288–289

conventional approach of structure 37

coping 368

core social identities 203–208

covariates 265–267, 271–272,

288–290

Cox’s model 289–290, 293–295

cultural variations 76, 212–213

culture 76, 250–253

data-analytical approach 375–377

data-analytical tool 377, 380

data collection 376

data-mining approaches 283, 309

data-mining method 300

data modeling culture 26, 264, 278, 300

decisional ambivalence 116–117

demographic analysis 283–288

demographic density 262

demographic events significance 155

demographic evolution 285

demography 261–263, 283

developmental data analysis steps 317

developmental psychology 315, 382

developmental regulation model 43

developmental scores 333–337

developmental shapes 324–333, 338–344

dialectical processes 69–70

discrete-time model 289

dispositional traits 239–242

diversification context of research status

108–110

divorce 110, 158–161, 168–169

double chain Markov model (DCMM)

295–296

dynamic determinants 344–348

dynamic structure 37–38

early adulthood through midlife 45–47

early childbearing 157–159

early marriage 166

early work investment 136–140

educational attainment 131, 134, 142,

144

educational influence 335–337

educational orientations 136–140

ego strength 61–62

emancipation 102

emerging adults 244

emotional valence 222, 225, 228–229

end of the life course 57, 60, 83–84

Note: See also last chapters of life

environmental proactivity 61

episodic memory recollection 227

episodic recollection 225–227

Erikson’s theory 121

event-based approach 263–264

event-based culture 265–273

event history analysis (EHA) 263,

265–269, 378

event-history model 300

event history regression models 288–295

event-sequential association rules mining

300–302

events to echo effects 181–182

evidence in life transitions 64–65

expectations 331–333

extended transition 211

external causality 18

extravert person 240

factor-analytic study 240

family relationship 45, 364

fertility project types 111

first-order homogenous transition

matrices 296–298

SUBJECT INDEX406

fitting latent difference score models

347–352

Five-Factor Theory (FFT) 371–373

formal aspects of life course 11–20

friends and acquaintances 210

gender 176

gender difference 5–6, 138

general developmental model 316

generalized linear model 293

generativity 61–62, 107, 121, 241, 248,

374

gerontology 20, 47–48

Gini heterogeneity index 275

good life course 72–77

group differences 160, 318, 338–344

growth curve 317, 320, 340

growth-curve analyses 321, 324–325

growth mixture model 342–343

hazard model 267–268, 309

hidden Markov model (HMM) 295,

297–298

hierarchical linear model 335, 378

highly generative American adults

247–250

historical force types 178

holistic approaches 26, 263

holistic culture 273–275

homogenous-Markov model 295

human agency 35, 131, 251

human individuality 238–242, 247

human infants 242

identity 24, 81–83, 100, 119, 197,

208–209, 211, 366, 370–374

identity change 24, 83, 197, 211, 383

identity formation process 209–210,

243

incomplete information 323–324

individual agency 362–365

individual differences 61, 221, 240

individual differences predictors

modelling 333–337

individual life course 273–274, 362

individual orientations and actions 133

individuals across time 371–372

individuals and life stories 374

induction tree 303–308

inheritance 118–119

inline transitions 174–175, 369

innovative methods of analysis 25–27

institutional framing 50–51

integrative life narrative 241

interdependent lives see linked lives

interdisciplinarity 361, 380–386

interdisciplinarity pathways 9–10

interdisciplinary perspectives 3

intergenerational ambivalence 102

intergenerational relations 93, 95–97

intergenerational relations research

module 100–104

intergenerational-social transition 303

interlacing transition 176, 178, 182, 185

Interlacing transitions and

contemporary discourse 186

internal causality 18

International Union for the Scientific

Study of Population 279, 286

introversion/extraversion 239–240

Keane and Wolpin model 273

knowledge provided by induction tree

306–307

Knowledge-Verbal score 348

Kohlberg’s theory 198–199

Konstanz Inheritance Survey 118

labor force 136–137

labor market 36, 111, 134, 150, 167,

176–178, 182

last chapters of life 71, 81

latent basis curves 328–329

latent curve model (LCM) 315, 317, 351

Subject Index 407

latent difference scores modeling

344–345

latent mixture model 318, 343–344

latent scores 325, 335–337

layered life course patterns 176–178

learning theory 198

leave taking 178–180

Liberal Market States 49–50

life course analysis 173, 261, 303, 369

life course context 375

life course data 265, 283, 375, 377

life course events 11, 17–20

life course pattern 176–178

life course perspective 83, 119, 173, 308

life course research 3–5, 361

life course shaping elements 261

life course sociology 36, 38

life course stages 11, 13–15

life course terminology 58

life course theories 63, 244

life course trajectories 76, 133, 267, 278

life course transition 155, 185, 197, 227,

383

life event inventory 223, 234

life event research 19

life events moderation 162–170

life narrative 239, 246–247

life review 120, 230

life-span developmental psychology 223,

315

life-span psychology 42

life stories 245–250

Life Story Interview 245

life table 285

life trajectory 373

lifetime period 219

linear growth models 324–327

linear spline models 324–331

linked lives 6, 12, 22

living context 217, 222

lone motherhood 182–187

long-suppressed tendencies 244

longitudinal-analytical tools 308

longitudinal data 26, 199, 299–300, 317,

320, 324, 338, 379

longitudinal life-span data 315

longitudinal-regression model 288

loose coupling 63–64, 66, 168

macro–micro links 5

Note: See also micro–macro links

macrostructural perspective of life

course 131–132

making sense of agency 67–69

management of change 368

marital disruption 158–159

marital status 182–185

Markov transition model 288, 295–296,

379

Marriage 18–19

masculine trait 61–63

maternity and parental leave 178

memorized life transition 219

memory and personality relationship

226–227

memory research 219

methodological approach 374

methodological preliminaries of research

104–105

methodological strategy 160

methods of analysis 25–27

micro–macro links 133

Note: See also macro–micro links

microfication 47

middle age 224–225

mixed-effects model 27, 310, 334

mixture model 342–343

mixture transition distribution (MTD)

295

MLE parameter 337

mobility 285, 303

model-based expectations about growth

331

model-restricted fuzzy-set cluster

analysis 342

modeling approaches 267

SUBJECT INDEX408

monothetic divisive algorithm (MDA)

274

moral reasoning 199

mortality tables 283–284

mother–adult daughter relationship 114

motherhood 179–184

movement through social space 384

MTD model 295

multi-level model 26, 378–379

multi-level modeling 290–293

multilevel and multiprocess modeling

265–269

multilevel growth model 334–335

multiple ambivalence 117

multiple group perspective 338–340

multiple-regression analyses 223

multiprocess model results 268

narrative 250–253

narrative approaches 13, 237–239,

252

narrative identities 241–244

national identity 202, 249–250

negative transition 228

neuroticism 223, 225–226

new father 187

new–old view of intergenerational

relations 93

nonlinearity 328–331

non-normative event 157–159, 364

non-normative life course 155

nonparametric stage 271

Non-Verbal abilities 348

normative transition 382–383

objective facts versus subjective

reconstructions 218–221

observed data 321

occupational career structures 178

off-time transitions 112

old age 47–49, 222–229

ontogenesis 13

ontogenetic push factors 385

operationalizing the life course 77–81

optimal matching 26, 274, 277–278, 309,

379

overcoming resistance 64

parent–child relationship 93, 96,

106–107, 109

part-time work 142, 179, 363

partnership translations 228

past life transition 219, 222

Person’s Education 337

personal ambivalence 116–117

personal attribute models 38

personal constructs 132

personal relationship 96, 116

personality 101, 133, 198,

205, 217, 221–222, 238,

245, 363, 370

personality and identity 370–371

personality–cognition relation 226

personality dimensions 371–372

personality psychology 239, 245, 371,

384

physical displacement 212–213

planful competence 21, 40, 68, 132

political engagement 209

political identity 209–210

polynomial model 328, 333

polynomial nonlinearity in growth 328

Population Index database 296

post-modernity 98

potential predictor 303–304

predictors of early work 136

pre-life course perspective 84

principle of life course 63–64

production of life 60–61

program evaluation literature 269–272

propensity scores matching 271

proportional-hazard model 289

proportional hazard-odd model 289

prototypical transitions 198–200

psychological analyzer 365–370

psychological well-being 223–225

Subject Index 409

random-coefficients 334–335

random effect model 292, 310

rational choice theory 65

reconstructive recollection 229

redemption sequences 246–247

redemptive self themes 248–249

regression analyses 112, 225, 277

regression tree 303

regulation of well-being 217–218

residuals 331

response trajectories 169–170

responsibility 65–67

retraditionalization 369

retrospective data 376

rituals of transition 208, 212

role of the state 187

SCT and SIT 202

second demographic transition 285

second-order homogenous transition

296

self-categorisation 200–201, 373

Self-Categorisation Theory (SCT) 24,

197, 201, 203

self-concept 372

self-defining memories 246

sequence analysis 263, 274

sequence-mining approach 300

sequential institutionalization 51

shaping life courses 261

shared conception 72

shared heterogeneity 290–293

simple linear regression 291

simultaneous institutionalization 51

Six-Foci Model (SFM) 372, 374, 383

slopes as outcomes 335

small-world phenomenon 204

social address model 38

social analyzer 365–370

social behavior 363

social categorisation 200, 203–204

social demographers 9

social environment 372

social identity 24, 197, 200, 208, 211, 373

social identity change 197, 211–213

Social Identity Theory (SIT) 200

social-mobility analysis 299

social niche model 38

social psychological perspective 131,

370, 383

social psychology 8, 14, 16, 18, 197, 200,

385

social sciences 157, 237, 283

social structure 35, 37–39, 41, 64,

69, 362

social theory 59

social transition analysis with induction

trees 303

socialization 9, 70, 367, 381

socioeconomic achievement trajectories

140–144

sociology 67

socio-personal framing of transition 174

solidarity 102

solidarity perspective 95

specific life periods 44–49

stage theories 237–238

stage transition 199

statistical modeling of life events 288

statistical models 351

status attainment 135

status changes 81–83

status definitions to ambiguity 181

stopped working 179–180

stories 250–251

story development 242–245

storytelling 239, 242, 247, 252

strands of life course 185–189

stratification algorithmic modeling

culture 264

structural agency 363–365

structural ambivalence 117

structural equation modeling (SEM)

techniques 317

structure and agency see agency and

structure

SUBJECT INDEX410

structure 36–52

studying lives in time 237

subjective meanings of lives 238

survival analysis 378

teenage childbearing 157–158, 168–169,

269–270

temporal coherence 243

the good death 72–73

thematic coherence 243–244

theoretical bridges 383–384

theoretical linkage of interdisciplinarity

10–11

theory-making in social sciences 252

timeframes 186

time-stamped event 288

timing of events 156, 174, 369

traditional demographic analysis 286

trait conception 238

transition 11–13, 21–24, 188–189, 197,

365–370, 381–385

transition analysis instruments

187–188

transition characteristics 15

transition process 174, 211–213

transition reduction 175–176

transition to adulthood 131, 144–149,

156, 225, 262, 269

transitions and life events 218–221

Transitions and Life Perspectives in

Middle Age 224

transversal substantive themes 20–27

transversal themes 381–383

tree-growing principle 304

true novel 243

turning points 110–113, 178

two-staged latent class analysis 144

U.N. Convention on the Rights of the

Child 45, 48

understanding lives 252

unexplained variance 63–64, 83–84

United Nations Principles for Older

Persons 48

unplanned pregnancy 163–164

Verbal-Knowledge score 320–321, 331

Verbal–Non-Verbal bivariate coupling

model 347

vignettes use 105

Warmr process 301–302

welfare-state regime 49–50

well-being 222

work investment 136–144, 149–150

work pattern 137–138, 140–142, 146

World Fertility Survey (WFS) 262

Youth Development Study (YDS), the

135–136

youth 46, 94, 100, 134, 138, 139

Subject Index 411


412

Toward an interdisciplinary perspective

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