Causal Ordering of Physical Self-Concept and Exercise Behavior:Reciprocal Effects Model and the Influence of Physical Education Teachers

Herbert W. MarshOxford University and University of Western Sydney

Athanasios PapaioannouDemocritus University of Thrace

Yannis TheodorakisUniversity of Thessaly

Does prior physical self-concept influence subsequent exercise behavior? On the basis of a large sampleof physical education classes (2,786 students, 200 classes, 67 teachers) collected early (Time 1) and late(Time 2) in the school year, findings support a reciprocal effects model in which prior physicalself-concept and exercise behavior both influence subsequent physical self-concept and exercise behav-ior. Whereas variables from the theory of planned behavior (TOPB; behavioral intentions, perceivedbehavioral control, exercise attitudes) also contributed to the prediction of subsequent exercise behavior,the effect of prior physical self-concept was significant for subsequent outcomes after controlling thesevariables, suggesting that the TOPB should be supplemented with self-concept measures. On the basis ofmultilevel models, there were systematic differences in these variables for students taught by differentteachers that generalized over time and across different classes taught by the same teacher. Support forthe reciprocal effects model was robust.

Keywords: reciprocal effects model, physical self-concept, physical activity, structural equation model-ing, theory of planned behavior

A positive self-concept is valued as a desirable outcome in manysettings across different disciplines of psychology such as health,sport/exercise, educational, developmental, clinical, and socialpsychology. In physical education and sport/exercise settings, self-concept is frequently posited as a mediating variable that facilitatesthe attainment of other desired outcomes such as exercise behav-ior, exercise adherence, or health-related physical fitness throughits influence on task choice, motivation, sustained effort, inten-tions, and persistence. The rationale behind this research is thatindividuals who feel positively about themselves in a particulardomain—the physical domain in this study—are more likely topursue and achieve desirable outcomes in that domain than indi-

viduals who do not feel positively about themselves. Historically,research in the physical domain focused on global self-esteem, butmore recently there has been a stronger emphasis on physicalself-concept measures designed specifically for sport and exercisesettings (Fox & Corbin, 1989; Marsh, 1997, 2002; Sonstroem,1978, 1997; Sonstroem, Harlow, & Salisbury, 1993), providingclear evidence for their convergent and discriminant validity inrelation to other self-concept domains (e.g., academic) and tosport/exercise outcome measures.

This potential role of physical self-concept in promoting exer-cise behavior is a critical issue. Physical inactivity and sedentarylifestyles—leading to poor physical fitness, obesity, and a multi-tude of related health problems—constitute a worldwide healthproblem for which traditional preventive-medicine interventionshave had limited success (Blair et al., 1996; Bouchard, Shephard,& Stephens, 1994). The 1996 U.S. Surgeon General’s (1996)report on physical activity and health singled out the need forresearch on psychological factors that influence adoption of a moreactive lifestyle and the maintenance of such behavior. Relatedconcerns are also evident in the shift in emphasis in sport/exerciseand physical education research from a narrow focus on sport to abroader focus on health-related outcomes. Despite promotion ofthe health benefits of physical activity, individuals have difficultystarting, and adhering to, exercise programs. Hence, researchersand practitioners have increasingly emphasized psychological con-structs such as self-concept, enjoyment, intrinsic motivation, andquality of life as important means to increasing physical activity.Marsh and Peart (1988) demonstrated that interventions that si-multaneously seek to enhance both physical self-concept and phys-ical fitness are more successful than interventions that focus ex-

Herbert W. Marsh, SELF Research Centre, University of Western Syd-ney, Bankstown Campus, New South Wales, Australia, Educational Stud-ies, Oxford University, Oxford, United Kingdom; Athanasios Papaioan-nou, Department of Physical Education and Sport Science, DemocritusUniversity of Thrace, Komotini, Greece; Yannis Theodorakis, Departmentof Physical Education and Sport Science, University of Thessaly, Trikala,Greece.

Research for this article was supported by the Greek Ministry of Edu-cation (Center of Educational Research). Herbert W. Marsh’s involvementwas supported by the Australian Research Council. We thank the manycolleagues from the University of Thrace and the University of Thessalywho collaborated in the planning and implementation of the project as wellas Joan Duda and Ken Fox, who were external advisers to the project. Wewould also like to thank, with noted importance, the many physicaleducation teachers and students who graciously volunteered to participate.

Correspondence concerning this article should be addressed to HerbertMarsh, Education, Oxford University, 15 Norham Gardens Road, OxfordOX2 6PY, United Kingdom. E-mail: herb.marsh@uws.edstud.ox.ac.uk

Health Psychology Copyright 2006 by the American Psychological Association2006, Vol. 25, No. 3, 316–328 0278-6133/06/$12.00 DOI: 10.1037/0278-6133.25.3.316

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clusively on physical fitness. As emphasized by Hagger,Chatzisarantis, Culverhouse, and Biddle (2003; Biddle, 2001),some of the aims of physical education in school settings are toreinforce student participation in health-related physical activityoutside of school and to help students develop lifelong healthylifestyles. Although physical education teachers and programs arein a unique position to pursue this aim, there is little research onhow effective they are at accomplishing it.

Juxtaposition of the Reciprocal Effects Model and theTheory of Planned Behavior (TOPB)

Empirical, theoretical, and methodological bases for the presentinvestigation come in part from an extensive body of self-conceptresearch, in which there has been a long-standing debate aboutwhether academic self-concept is a determinant or a consequenceof academic achievement. Calsyn and Kenny (1977) contrastedself-enhancement and skill-development models. The self-enhancement model posits self-concept as a primary determinantof academic achievement (i.e., self-concept 3 achievement). Incontrast, the skill development model implies that academic self-concept emerges principally as a consequence of academicachievement (i.e., achievement 3 self-concept). Largely becauseof limitations in statistical techniques used in the 1980s to testthese models, researchers argued for either–or conclusions. In areview and critique of this research, Marsh (1990a, 1990b, 1993;also see Marsh, Byrne, & Yeung, 1999) argued that much of thisresearch was methodologically unsound and inconsistent with self-concept theory. He argued that a more realistic compromise be-tween the self-enhancement and skill-development models was areciprocal effects model in which prior self-concept affects sub-sequent achievement, and prior achievement affects subsequentself-concept. A growing body of research (see review by Marsh etal., 1999) has established support for the reciprocal effects modelthat has major implications for the importance placed on self-concept as a means of facilitating other desirable outcomes as wellas its use as an important outcome variable.

Support for the reciprocal effects model is based largely onacademic self-concept research, but its underlying rationale isconsistent with the historically important work in the physicaldomain by Sonstroem (1978) and his subsequent path-analytictests of his theoretical model (Sonstroem et al., 1993; also seeSonstroem, 1997). More recently, Chanal, Marsh, Sarrazin, andBois (2005; also see Marsh & Craven, 2005) pursued tests of thereciprocal effects model in relation to physical self-concept andperformance skills in physical education classes. In support of thereciprocal effects model, they demonstrated that gymnastics self-concept and gymnastics performance collected at the start of agymnastics training program (Time 1, or T1) each had significanteffects on gymnastics self-concept and gymnastics performancecollected at the end of the 10-week program (Time 2, or T2).Achievement was based on videotapes of each student’s perfor-mance on a standardized gymnastics performance test that wasevaluated by three independent expert judges. Thus, gymnasticsself-concept and gymnastics performance were both determinantsand consequences of each other, and these results generalized overresponses by boys and girls and by younger and older students.Marsh and Perry (2005) extended research on the reciprocal effectsmodel to the championship performances of 275 of the world’s top

swimmers from 30 countries. They demonstrated that prior athleteself-concept had a significant effect on subsequent swimmingperformance at two international championships beyond the verystrong influence of prior personal best performance in the sameevent.

The overarching purpose of the present investigation is to testthe reciprocal effects model in relation to physical self-concept,exercise behavior, and health-related physical activity. On thebasis of the reciprocal effects model, we predicted that (a) priorphysical self-concept would influence subsequent exercise behav-ior beyond the contribution of prior exercise behavior and (b) priorexercise behavior would influence subsequent physical self-concept beyond the contribution of prior physical self-concept.

Motivational models such as Ajzen’s (1988, 1996) TOPB havebeen influential in predicting how positive exercise attitudes andbehavioral intentions are translated into actual exercise behavior(e.g., Armitage & Conner, 2001; Hagger, Chatzisarantis, & Biddle,2002). TOPB posits that an intention to engage in a given behavioris the most immediate predictor of that behavior. Intentions arelargely shaped by attitudes toward behavior, that is, people’sassessment of their beliefs regarding the target behavior’s effec-tiveness in producing outcomes and an evaluation of these out-comes. Intention and behavior are also affected by perceivedbehavioral control, a construct representing people’s assessment oftheir capacities concerning their behavioral engagement that issimilar to traditional measures of self-efficacy (Bandura, 1997; butalso see Motl et al., 2002). Meta-analytic reviews in exercisebehavior indicate medium to large effect sizes for the intention–behavior, attitude–intention, and perceived behavioral control–intention relationships (Hagger et al., 2002) but suggest that mostof the effects of attitudes and perceived behavior control onbehavior are mediated through intentions. Accordingly, we in-cluded four constructs derived from the TOPB in the presentinvestigation: positive attitudes toward exercise, exercise percep-tions of behavioral control, exercise intentions, and actual exercisebehavior (Theodorakis, 1994). However, Hagger et al. (2003)noted that the TOPB does not account for all of the variation inexercise intention and behavior, suggesting that it might be usefulto incorporate constructs from other theoretical models such asperceived competence and self-concept. In offering this sugges-tion, they emphasized that such constructs might be redundant withperceived behavioral control so that it would be important toevaluate support for discriminant validity of any such new mea-sures in relation to existing TOPB measures before pursuingmodification of the TOPB model (also see Marsh, 1994).

For purposes of the present investigation, the main intent forincluding these TOPB variables was to determine whether physicalself-concept contributed to the prediction of exercise behaviorbeyond what could be explained in terms of the TOPB constructsthat have been shown to be important in prediction of exercisebehavior. Specifically, we predicted that (a) prior physical self-concept would influence subsequent exercise behavior beyond theeffects of TOPB constructs (prior exercise behavior, prior exercisebehavior intentions, prior exercise behavior attitudes, and priorexercise perceptions of behavioral control) and (b) prior exercisebehavior would influence subsequent physical self-concept beyondthe effects of TOPB constructs. Nevertheless, the research also hasthe potential to contribute to understanding of the TOPB. Hence,our a priori predictions—particularly the direct effect of physical

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self-concept beyond the effects that are mediated by TOPB con-structs—would support not only the importance of physical self-concept but also the inclusion of self-concept in TOPB studies.

The juxtaposition of the TOPB with the reciprocal effects modelemphasized in self-concept research identifies potential strengthsand limitations in both approaches. The reciprocal effects modelmakes explicit empirical tests of causal ordering between self-concept and outcomes through the application of multiwave paneldesigns in which the same constructs are collected from the sameindividuals on multiple occasions. In this respect, the ordering ofvariables within the path models is determined only on the basis oftheir temporal ordering. In contrast, in the TOPB, the causalordering of the critical constructs is typically based on theory andnot subject to empirical tests. Particularly in studies that consideractual behavior collected on a single occasion, it is unclear whetherpsychological constructs such as behavioral intentions, attitudes,and exercise perceptions of behavioral control are causes or con-sequences (or both) of behavior. Thus, Albarracin, Johnson, Fish-bein, and Muellerleile (2001) argued that “because researchershave used retrospective reports of past behavior as the criterionvariable, it is generally difficult to decide the extent to whichbehavior results from or leads to intentions and attitudes” (p. 144)and emphasized that further research with longitudinal designs isneeded. Even when there are prior and subsequent measures ofbehavior, there are still no tests of the causal ordering of the otherconstructs, even though the causal ordering has critical implica-tions for the interpretation of the results. Thus, for example, ifexercise attitudes have substantial effects on exercise behavior—even if they are mediated by behavioral intentions—then it isreasonable to target exercise attitudes as a means to enhanceexercise behavior. If, however, the direction of causality is fromexercise intentions to exercise attitudes (i.e., the relation betweenexercise attitudes and exercise behavior is spurious), then it makesno sense to target exercise attitudes as a means of enhancingbehavior. However, a potential weakness of the reciprocal effectsmodel (see Marsh et al., 1999) is an implicit assumption thateffects of self-concept on desired outcomes are mediated throughintervening motivational constructs such as those posited in theTOPB. Whereas this assumption is sometimes made explicit andtested empirically (e.g., Marsh et al., 1999), it is typically leftimplicit. Hence, bringing together the alternative approaches usedin tests of the reciprocal effects model from self-concept researchwith those used in TOPB research provides a potentially importantcontribution to each area of research.

A Multilevel Perspective

Methodologically, we introduce important advances in the ap-plication of multilevel modeling (also referred to as hierarchicallinear modeling). In health, education, sport/exercise, and thesocial sciences more generally, data typically have a multilevelstructure in which individual participants (e.g., athletes, students,patients, or other individual members of a group) are clustered intogroups (e.g., teams, classes, units, or gyms) that might be clusteredinto higher level administrative units (schools, states, countries,federations). In general, it is inappropriate to pool responses ofindividuals without regard to group unless it can be shown that thegroups do not differ significantly from each other. Typically,members of the same intact group are more similar to other

members of the same group than they are to members of differentgroups, or become so over time (even, perhaps, when initial groupassignment was random). If there are systematic differences be-tween groups, then the typical single-level analyses that ignore thisclustering of individuals into groups are likely to be invalid (vio-lating statistical assumptions in a way that increases the likelihoodof finding a significant effect where there is none). Furthermore,characteristics associated with individuals are likely to be con-founded with those based on groups.

From a practical perspective, a multilevel approach allows re-searchers to pursue new questions about how effects vary fromgroup to group and the group characteristics associated with thisvariation. This is particularly important in studies such as thepresent investigation in which some of the effects are likely to varyas a function of the particular teacher (instructor or class leader) or,perhaps, even the particular group of participants in each class.Increasing evidence in school effectiveness research demonstratesthat outstanding teachers can make substantial differences in theachievement levels of the students they teach. Indeed, Monk(1992) cited a number of studies in support of the observation that“one of the recurring and most compelling findings within thecorpus of production function research is the demonstration thathow much a student learns depends on the identity of the instructorto which that student is assigned” (p. 320). In physical educationclasses, we also speculate that teachers and their different ap-proaches can influence exercise behavior and physical activitylevels outside of the classroom (e.g., Marsh & Peart, 1988).Whereas teacher effectiveness and teacher characteristics are in-herently teacher-level variables, recent applications of multilevelmodeling provide examples of how to disentangle the effects ofteachers from the characteristics of individual students whom theyteach (Papaioannou, Marsh, & Theodorakis, 2004; Rowe & Hill,1998). Hence, the multilevel approach provides a more heuristicand statistically appropriate approach to issues related to exercisebehavior and its psychological determinants than would be possi-ble with traditional single-level approaches that ignore the clus-tering of individuals within groups (see Goldstein, 2003; Goldsteinet al., 1998; Raudenbush & Bryk, 2002.)

The Present Investigation

In the present investigation, we evaluate predictions from thereciprocal effects model of the causal ordering of physical self-concept and exercise behavior that are adapted from an extensivebody of academic self-concept research. Similar to the study ofgymnasts mentioned previously, this model (see Figure 1A) makespredictions about the effects of T1 physical self-concept and T1exercise behavior collected at the start of the school year on bothT2 physical self-concept and T2 exercise behavior collected at theend of the school year. The critical predictions distinguishing thereciprocal effects, self-enhancement, and skill development mod-els are the cross paths relating T1 physical self-concept to T2exercise behavior and T1 exercise behavior to T2 physical self-concept. The skill development model predicts that only the T1exercise behavior–T2 physical self-concept path will be signifi-cant; the self-enhancement model posits that only the T1 physicalself-concept–T2 exercise behavior path will be significant; and thereciprocal effects model predicts that both paths will be significant(see Figure 1A). Extending previous research on the reciprocal

318 MARSH, PAPAIOANNOU, AND THEODORAKIS

effects model, we combine it with variables posited in the TOPB,predicting that prior physical self-concept has a direct effect onsubsequent exercise behavior and that prior exercise behavior hasan effect on subsequent physical self-concept, even after control-ling prior measures of exercise attitudes, perceptions of behavioralcontrol, and behavioral intentions (see Figure 1B).

Method

Participants and Procedures

The present investigation was based on data from a large, nationallyrepresentative database of Greek primary, junior high school, and seniorhigh school students designed to provide a census of diverse outcomesrelevant to all levels of physical education (see Papaioannou, 2000; Papa-ioannou et al., 2004). Participants in the present investigation were 2,786students (50% male adolescents) from 200 physical education classes atdifferent levels of schooling (29% primary, 36% middle school, and 35%high school). The schools were randomly selected from the total number ofschools from nine different geographical areas of Greece, involving bothurban and suburban areas and different social classes, to be nationallyrepresentative of Greek students. To maximize the breadth and represen-tativeness of the sample relative to the number of teachers considered and

to eliminate potential problems of clustering effects in relation to testingmany teachers from the same school, we included only one physicaleducation teacher per school in the sampling design. Because most of thephysical education teachers in the present investigation taught more thanone class, we are able to disentangle the effects of a particular teacher froma particular group of students taught by the teacher.

T1 variables were collected shortly after the start of the school year(September–October, 1998) whereas T2 variables were collected near theend of the school year (April–May, 1999). T1 was at least 5 weeks after thebeginning of the school year so that most students had completed at least10 class sessions with the same teacher. At both times, the anonymousquestionnaires were distributed by nine research assistants and were com-pleted in the students’ classes. Student consent and permission from theMinistry of Education and the school authorities were required. An impor-tant complication in the present investigation was the requirement (by lawof the Greek Ministry of Education) that all questionnaires should becompleted anonymously. Hence, for purposes of the present investigation,T1 and T2 cases were matched on the basis of class identification, sex, anddate of birth. Because not all students provided a proper date of birth onboth occasions, there were a large number of cases that could not bematched. For present purposes, we considered only classes for which therewere at least 10 students at T1, at least 10 students at T2, and at least 5successfully matched cases with data for T1 and T2. Excluded wereteachers who did not participate in both data collections (teachers from afew schools had data from only T1) and classes that did not have the samephysical education teacher at T1 and T2. Next, we excluded all studentswho did not have matched T1 and T2 responses. Although the full data setconsisted of responses by 4,546 students (T1) and 4,390 students (T2), ouranalyses were based on responses by the 2,786 students who had success-fully matched T1 and T2 responses. It is important to emphasize that manyof the students who apparently had only T1 or only T2 responses actuallyhad both T1 and T2 responses but could not be matched on the basis ofavailable data. Furthermore, the critical relation between T1 physicalself-concept and exercise behavior was nearly identical for students whocould be matched (r � .26) and those who could not be matched (r � .25)with T2 responses. For students with successfully matched T1 and T2responses, there was little missing data. A growing body of research hasemphasized potential problems with traditional pairwise deletion, listwisedeletion, and mean substitution approaches to missing data (e.g., Graham& Hoffer, 2000; Little & Rubin, 1987), leading us to implement theExpectation Maximization Algorithm, the most widely recommended ap-proach to imputation for missing data, as operationalized with missingvalue analysis in SPSS. Because of the small amount of missing data forthese 2,786 students, different approaches applied to the problem of miss-ing data resulted in nearly identical results.

Measures

Because of the large scale of the overall project from which data in thepresent investigation are based, there was a necessity to reach an appro-priate compromise between brevity of the scales and psychometric rigor.Measures had previously been selected from the best available relevantmeasures in sport/exercise psychology, translated into Greek, and were thebasis of previous Greek research providing psychometric support for themeasures (Papaioannou, 2000; Papaioannou & Theodorakis, 1996; Theod-orakis, 1994; also see Marsh, Papaioannou, Martin, & Theodorakis, inpress; Papaioannou et al., 2004). For present purposes, five constructs areconsidered (see Appendix for a summary of coefficient alpha estimates ofreliability at T1 and T2 as well as test–retest correlations): exercise inten-tions (2 items; Theodorakis, 1994); exercise attitudes (3 items; Theodor-akis, 1994); exercise perceived behavioral control (3 items; Theodorakis,1994); actual exercise behavior in the last month (1 item; Theodorakis,1994); and physical self-concept (5 items; Fox & Corbin, 1989; adapted inGreek by Diggelidis & Papaioannou, 1999). The four TOPB constructs

Figure 1. Reciprocal effects model. A: Path model of relations betweenphysical self-concept (PSC) and exercise behavior (EBeh) at Times 1 and2 (T1 and T2). The skill development model predicts that the T1 EBeh3 T2PSC path will be significantly positive (�); the self-enhancement model positsthat the T1 PSC3 T2 EBeh path will be significantly positive (�); and thereciprocal effects model predicts that both T1 EBeh 3 T2 PSC and T1PSC 3 T2 EBeh paths will be significantly positive. All three theoreticalmodels predict that the horizontal paths (represented by dashed lines) willbe highly significant (��). B: Reciprocal effect model extended to includevariables from the theory of planned behavior: exercise attitudes (EAtt),exercise intentions (EInt), and exercise perceived behavioral control(EPBC). Because the main focus of this study is on the reciprocal effectsmodel, paths associated with new variables in the extended model arerepresented as gray lines.

319PHYSICAL SELF-CONCEPT AND EXERCISE

were based on Ajzen’s (2002) instructions for constructing a TOPBquestionnaire.

Marsh, Papaioannou, Martin, and Theodorakis (in press; Papaioannou etal., 2004; also see Appendix) reported a confirmatory factor analysis andpsychometric analyses of measures from the overall project that includedscales used in the present investigation. For the constructs considered here,they demonstrated an excellent fit of the data to the a priori factor structure,good reliability (median � � .81), reasonable support for stability over thecourse of the school year (median test–retest stability � .58), and goodsupport for convergent and discriminant validity in relation to time andother constructs considered in the overall project. Hence, there seems to bea good compromise between brevity of scales that was necessary in thislarge-scale project and psychometric rigor, and support for the psychomet-ric effectiveness of the measures in relation to typical practice in sport andexercise research (e.g., Ostrow, 1990). Whereas reliance on a single itemto measure exercise behavior may be potentially problematic, Ajzen (2002)stressed that the behavioral criterion variable must correspond as closely aspossible to the predictor variables so that it is typical to measure behaviorwith a single item (see discussion by Kimiecik, 1992, supporting thereliability and validity of physical activity measures like that used here).Whereas the pattern of relations with other variables was supportive of itsconstruct validity and whereas test–retest correlation over the course of theschool year was reasonable (.41), results based on a single-item scale arelikely to be less reliable than a multi-item scale so that the effect sizesobserved here are more likely to be smaller and more conservative thanresults that would have been obtained if a multi-item measure had beenused.

Statistical Analysis: Multilevel Modeling

Multilevel modeling analyses were conducted with the commerciallyavailable MLwiN statistical package (Goldstein, 2003; Goldstein et al.,1998; also see Raudenbush & Bryk, 2002). Like those traditionally used toevaluate the reciprocal effects model in previous self-concept research(Marsh & Craven, 2005; Marsh et al., 1999), the major focus of ourmultilevel analyses is on a set of multivariate longitudinal path models totest the effects of prior physical self-concept and exercise behavior at thestart of the school year on these same outcomes at the end of the schoolyear (see Figure 1A). Hence, these are conditional models of change inwhich the effects of T1 outcomes are partialed out of T2 outcomes (seeMarsh, Hau, & Kong, 2002, for further discussion of conditional andunconditional models of change in multilevel models). In the multilevelpath analysis models used in the present investigation, demographic vari-ables (sex and age) came first in the causal ordering, whereas the orderingof all other variables was determined by their temporal ordering only (i.e.,T1 variables collected at the start of the school year preceded T2 variablescollected near the end of the school year). We began with a variancecomponents model that had no predictor variables but indicated how muchof the variation in each outcome could be attributed to the teacher (Level3), different classes taught by the same teacher (Level 2), and to theindividual students (Level 1). We note, however, that because—by de-sign—there was only one teacher sampled from each school, it was notpossible to disentangle pure teacher effects from school effects. Hence,what we refer to as teacher effects could be argued to represent teacher–school effects. However, because school effects are likely to be constantover the school year, changes during the school year (T2 outcomes cor-rected for T1 outcomes) are likely to be a function of the teacher rather thanthe school. In subsequent models, T1 physical self-concept and exercisebehavior were added to the model, along with student sex (male adoles-cents � �1, female adolescents � 1), age, and potential interveningvariables (exercise attitudes, exercise intentions, and exercise perceptionsof behavioral control). Several data transformations were conducted tofacilitate interpretations and infer interaction effects. We began by stan-dardizing (z scoring) all variables to have M � 0, SD � 1 across the entire

sample, separately at T1 and T2 (see Marsh & Rowe, 1996; also see Aiken& West, 1991; Raudenbush & Bryk, 2002). We then represented interac-tion effects as the product of individual (z score) standardized variables(the product terms were not restandardized) to reduce problems associatedwith multicollinearity.

Results

Teacher, Class, and Student Effects: IntraclassCorrelations

Initial analyses (see Table 1) determined the effects of theteacher and class for the variables considered here. Because almostall teachers taught several different classes, the results also pro-vided the unusual opportunity to unconfound the effects of theteacher and the group of students in a particular teacher’s class. Tothe extent that individual class effects can be explained in terms ofthe teacher, we expect the teacher variance components should besubstantially larger than the class variance components. The resultsalso provide the opportunity to evaluate the stability of these class-and teacher-level effects over time in analyses of T1 and T2responses. Whereas teacher and class effects on any one occasionare likely to reflect a combination of true teacher effects andpreexisting differences in the nonrandom composition of eachclass and school, T2 effects after controlling for T1 effects (i.e.,conditional change) are more likely to reflect teacher effects thatare independent of these initial differences (Marsh et al., 2002).

Intraclass correlations and variance components (see Table 1)provided an index of teacher and class effects—whether ratings bystudents within the same physical education class were similar toeach other and differed systematically from those by students inother classes. For physical self-concept, the intraclass correlationsfor T1 responses (.12) and T2 responses (.09) were moderate; 12%and 9% of the variance in physical self-concept responses at T1and T2, respectively, were explained in terms of a combination ofteacher and class effects. In each case, most of the variance wasdue to the teacher rather than the group of students in a particularclass; the class effect was not statistically significant at either T1or T2. Changes in physical self-concept over time (i.e., T2 scoresafter partialing out the effects of T1 scores) had an intraclasscorrelation of .04, indicating that 4% of the variance in self-concept change over the school year was explained in terms of theteacher and class effects. Again, however, the variance compo-nents associated with the teacher were statistically significant,whereas those associated with the class were not.

The next four outcome variables (exercise attitudes, exerciseperceptions of behavioral control, exercise intentions, and actualexercise behavior) came from the TOPB. The intraclass correla-tions were very small for exercise attitudes but larger for intentionsand actual exercise behaviors, particularly for exercise perceptionsof behavioral control. For each of these three variables, teachereffects were significant, whereas class effects were smaller andtypically not statistically significant. Particularly for T2 behavior,intentions, and perceptions of behavioral control after controllingT1 outcomes, there were significant effects due to the teacher andno significant effects due to the different classes taught by thesame teacher.

In summary, the intraclass correlations and variance compo-nents provided support for teacher effects on the set of individualstudent outcomes over the course of the school year. Teacher

320 MARSH, PAPAIOANNOU, AND THEODORAKIS

effects were systematically larger than class effects, indicating thatteacher effects generalized across different classes taught by thesame teacher.

Tests of the Reciprocal Effects Model

Results from a series of models designed to test the reciprocaleffects model for physical self-concept and exercise behavior aresummarized in Table 2. In Model 1, T1 physical self-concept andT1 exercise behavior are used to predict the corresponding T2scores. Not surprisingly, the largest effect on T2 physical self-concept is T1 physical self-concept (.43), and the largest effect onT2 exercise behavior is T1 exercise behavior (.33). These arerepresented by the horizontal paths (dashed lines) in Figure 1A. Ofgreater interest, however, are the cross paths (represented by solidblack lines in Figure 1A). Consistent with the predictions from thereciprocal effects model, the T1 physical self-concept–T2 exercisebehavior (.17) and the T1 exercise behavior–T2 physical self-concept paths (.10) are both substantial and highly significant.

In Models 2 and 3 (see Table 2), we evaluated the main effectsof sex, age, and their interaction. For both T2 physical self-conceptand exercise behavior, girls had lower scores than boys, and thescores declined with age. There were no significant Age � Sexinteractions for physical self-concept, but for exercise behavior,the sex difference grew somewhat larger with age. Althoughsubstantively important in their own right, our main concern inModel 3 was how the inclusion of age and sex influenced supportfor the reciprocal effects model. Although the pattern of results insupport of the reciprocal effects models is still evident (all pathsare statistically significant), the sizes of these effects are somewhatsmaller when controlling for age and sex. Because sex and ageclearly come before the T1 variables in the causal ordering, someof the effects in support of the reciprocal effects model in Model1 were spurious relations that should be attributed to sex and age.The effects of sex and age on T2 outcomes were smaller in Model3, which includes T1 physical self-concept and behavior, than inModel 2, which excludes T1 physical self-concept and behavior.Because sex and age clearly come before the T1 variables in the

Table 1Variance Components (Var) Attributable to Differences Due to Teachers, Different ClassesTaught by the Same Teacher, and Individual Students on All Outcome Variables

Outcome variable

T1 T2Change between

T2 and T1

Var SE Var SE Var SE

Physical self-conceptTeacher .11* .02 .07* .02 .02* .01Class .01 .01 .02 .01 .01 .01Student .88* .02 .91* .03 .74* .02Intra corr .12 .09 .04

Exercise behaviorTeacher .08* .02 .10* .02 .07* .02Class .03* .01 .01 .01 .00 .01Student .89* .03 .89 .03 .77* .02Intra corr .11 .11 .09

Exercise attitudesTeacher .02* .01 .01 .01 .01 .01Class .03* .01 .00 .01 .00 .01Student .95* .03 .99 .03 .89 .03Intra corr .04 .01 .01

Exercise intentionsTeacher .07* .02 .10* .02 .04* .01Class .04* .01 .02 .01 .01 .01Student .89* .03 .88* .02 .71* .02Intra corr .11 .12 .06

Exercise perceivedTeacher .11* .03 .17* .03 .05* .01

Behavioral controlClass .05* .02 .02* .01 .01 .01Student .84* .02 .81* .02 .63* .02Intra corr .16 .19 .09

Note. For each outcome variable, individual student responses were evaluated in terms of the proportion ofvariance that can be explained by the teacher, the different classes taught by the same teacher, and residualvariance due to individual students. Separate analyses were done for Time 1 (T1) responses, Time 2 (T2)responses, and change in responses over the T2–T1 interval (i.e., predicting T2 responses with T1 responses asa predictor variable). Variance components more than twice their standard errors are statistically significant ( p �.05). Intraclass correlation is proportion of variance due to teacher and class effects (i.e., sum of the variancecomponents for teacher and class divided by the sum of the variance components for teacher, class, and students).Intra corr � intraclass correlation.* p � .05.

321PHYSICAL SELF-CONCEPT AND EXERCISE

causal ordering, the effects of sex and age on T2 outcomes are saidto be mediated by the same outcomes collected at T1. It isimportant to note, however, that all the effects of T1 physicalself-concept and exercise behavior continue to be statisticallysignificant on T2 physical self-concept and exercise behavior, evenafter controlling the effects of sex and age.

In Model 4, we evaluated moderated effects—tests of whetherthe effects of prior physical self-concept and exercise behaviorvary as a function of (i.e., interact with) sex and age. Although wetested a total of 12 interaction effects in Model 4, only 2 werestatistically significant. The effects of T1 physical self-concept onT2 physical self-concept and the effects of T1 exercise behavior onT2 exercise behavior were more positive for older students. Hence,each of these variables was more stable for older students. It isimportant to note, however, neither the effect of prior self-concepton subsequent exercise behavior nor the effect of prior exercisebehavior on subsequent self-concept varied as a function of sex orage. Hence, support for the reciprocal effects model generalizedover sex and age.

In summary, results from all four models provided clear supportfor the reciprocal effects model: Prior self-concept had significanteffects on subsequent exercise behavior, and prior exercise behav-ior had a significant effect on subsequent self-concept. Whereassome support for the reciprocal effects in the initial Model 1 wasbased on spurious relations that should have been attributed to sexand age, the pattern of results was still clear even after controllingthese spurious effects. Whereas there were some significant inter-actions with age in Model 4, support for the reciprocal effectsmodel was still strong and the critical cross paths—effects ofself-concept on exercise behavior and of exercise behavior on

self-concept—did not interact with sex or age and were unaffectedby the inclusion of the interaction terms.

Juxtaposition of the Reciprocal Effects Model and theTOPB

Reciprocal effects of physical self-concept. The intent of Mod-els 5–7 (see Table 3) was to provide a preliminary evaluation ofthe reciprocal effects between physical self-concept and each ofthe three additional constructs based on the TOPB. Results alreadysummarized in Table 2 (see Model 3) demonstrate clear supportfor the reciprocal effects of physical self-concept and exercisebehavior. In Model 5, there was no support for the reciprocaleffects model in that there was only a weak effect of T1 exerciseattitudes on T2 physical self-concept and no significant effect ofT1 physical self-concept on T2 exercise attitudes. In Model 6,there was strong support of the reciprocal effects model in that T1physical self-concept had a significant effect on T2 exercise be-havior intentions, and T1 exercise behavior intentions had a sig-nificant effect on T2 physical self-concept. Similarly, in Model 7,there was strong support for the reciprocal effects model of rela-tions between physical self-concept and exercise perceived behav-ioral control. In summary, prior physical self-concept had signif-icant effects on subsequent measures of exercise behaviorintentions, exercise perceived behavioral control, and actual be-havior beyond that which could be explained in terms of priormeasures of these variables, whereas all four of the TOPB vari-ables at T1 had an impact on T2 physical self-concept.

Reciprocal effects of TOPB variables. In Model 8 (see Table4), we considered reciprocal effects relating only the four TOPB

Table 2Effects of Time 1 Predictor Variables on Time 2 Outcome Variables: Reciprocal Effects Model

Predictor (Time 1) variable

Time 2 outcome (dependent) variables

Model 1 Model 2 Model 3 Model 4

PSC EBeh PSC EBeh PSC EBeh PSC EBeh

� SE � SE � SE � SE � SE � SE � SE � SE

Fixed effectsPSC .43* .02 .17* .02 .38* .02 .12* .02 .38* .02 .11* .03EBeh .10* .02 .33* .02 .09* .02 .33* .02 .09* .02 .34* .02Age �.21* .04 �.25* .03 �.10* .02 �.20* .02 �.10* .02 �.19* .02Sex (male adolescent � �1,

female adolescent � 1) �.26* .03 �.16* .03 �.15* .02 �.08* .02 �.15* .02 �.08* .02Age � Sex �.03 .02 �.06* .02 .00 .02 �.04* .02 .02 .02 �.03 .02PSC � Sex .03 .02 �.03 .02PSC � Age .06* .02 .02 .02EBeh � Sex .00 .02 .04 .02EBeh � Age .01 .02 .08* .02PSC � EBeh .01 .02 �.01 .02

Residual varianceTeacher .02* .01 .05* .02 .02* .01 .04* .02 .01 .01 .02 .02 .01 .01 .02* .01Class .01 .01 .00 .01 .01 .01 .01 .01 .01 .01 .00 .01 .01 .01 .00 .01Students .71* .03 .72* .03 .85* .02 .86* .03 .72* .02 .75* .02 .71* .02 .74* .02

Note. All outcome and predictor variables were standardized (M � 0, SD � 1) so that beta weights correspond to standardized beta weights. All betaweights are statistically significant when they differ from zero by more than two standard errors (SEs). PSC � physical self-concept; EBeh � exercisebehavior.* p � .05.

322 MARSH, PAPAIOANNOU, AND THEODORAKIS

variables at T1 to measures of the same constructs collected at T2.There was a pattern of reciprocal effects relating most of thesevariables. Notable, however, was the lack of effects of T1 physicalattitudes on any of the TOPB variables at T2. In comparison, theeffects of T1 exercise behavior, T1 exercise behavior intentions,and T1 exercise perceived behavioral control all had significanteffects on all three of these corresponding T2 measures.

Reciprocal effects of physical self-concept and TOPB variables.Finally, in Model 9 (see Table 4), we incorporated the four TOPBvariables and physical self-concept into a single path model (seeFigure 1B). Because the focus of this study is on physical self-concept, these effects are of particular interest. These resultsprovide clear support for the effects of T1 physical self-concept onthree of the four T2 TOPB variables (exercise behavior, exerciseintentions, and exercise perceived behavioral control) but not T2exercise attitudes. There were also significant paths leading to T2physical self-concept from two of the four T1 TOPB variables (T1exercise behavior and T1 exercise perceived behavioral control)but not for T1 exercise attitudes or T1 exercise intentions. Becausethe path leading from T1 physical self-concept to T2 exercisebehavior was statistically significant—even after controlling forthe additional TOPB and demographic variables—there was clearsupport for the reciprocal effects model of relations betweenphysical self-concept and exercise behavior.

Exercise behavior is a particularly important outcome and cen-trally relevant for both the reciprocal effects model and the TOPB.Whereas paths leading to T2 exercise behavior were significant forT1 exercise intentions and T1 physical self-concept, those from T1exercise attitudes and T1 exercise perceived behavioral controlwere not significant. It is important to note that, whereas thelargest effect of T1 exercise behavior was on T2 exercise behavior,

paths leading from T1 exercise behavior were significant for allfive T2 outcome variables. This is, of course, consistent with thelogic of the reciprocal effects model and illustrates why it isimportant to evaluate causal ordering with multiwave (longitudi-nal) models in which each of the variables under consideration iscollected on at least two occasions.

Exercise intentions are also very important in the TOPB in thatall or most effects on exercise behavior are predicted to be medi-ated through exercise intentions. Results (Model 9) provide rea-sonably good support for these predictions. Consistent with thecentral role of behavioral intentions, four of the five T1 variables(all but exercise attitudes) had significant effects on T2 behavioralintentions. Similarly, T1 behavioral intentions had significant ef-fects on four of five of the T2 outcomes variables (all but exerciseattitudes). However, consistent with the reciprocal effects model,T1 physical self-concept had a direct effect on T2 exercise behav-ior beyond effects that might have been explained in terms ofTOPB variables.

Perceived behavioral control also plays a key role in the TOPB.Although the effect of T1 perceived behavioral control on T2exercise behavior was not statistically significant, it had the largesteffect of any T1 variable on T2 exercise intentions (other than, ofcourse, T1 exercise intentions). However, there was also a patternof reciprocal effects relating T1 physical self-concept and T1exercise perceived behavioral control to the corresponding T2outcome values.

In comparison to the other four variables considered in Model 9,exercise attitudes played a minor role. T1 exercise attitudes had noeffects on any T2 outcomes other than T2 exercise attitudes. Incontrast, there were significant paths leading to T2 exercise atti-tudes from T1 exercise activity and T1 exercise intentions, as well

Table 3Effects of Time 1 Predictor Variables on Time 2 Outcome Variables: Tests of Reciprocal Effects Between Physical Self-Concept andVariables from the Theory of Planned Behavior (Exercise Attitudes, Exercise Intentions, Exercise Perceptions of Behavioral Control)

Predictor (Time 1) variables

Time 2 outcome (dependent) variables

Model 5: attitudes Model 6: intentions Model 7: behavior control

PSC EAtt PSC EInt PSC EPBC

� SE � SE � SE � SE � SE � SE

Fixed effectsPSC .39* .02 .03 .02 .37* .02 .11* .02 .36* .02 .13* .02EAtt .04* .02 .33* .02EInt .09* .02 .42* .02EPBC .11* .02 .44* .02Age �.10* .02 �.05* .02 �.08* .02 �.16* .02 �.07* .02 �.19* .02Sex (male adolescents � �1,

female adolescents � 1) �.16* .02 .06* .02 �.15* .02 �.03 .02 �.16* .02 �.02 .02Age � Sex �.01 .02 �.01 .02 �.01 .02 .06* .02 �.01 .02 �.05* .02

Residual varianceTeacher .02* .01 .00 .01 .02* .01 .01 .01 .02* .01 .01 .01Class .01 .01 .00 .01 .01 .01 .00 .01 .01 .01 .00 .01Students .72* .02 .89 .03 .72* .02 .69 .02 .71* .02 .61 .02

Note. All outcome and predictor variables were standardized (M � 0, SD � 1), so that beta weights correspond to standardized beta weights. All betaweights are statistically significant when they differ from zero by more than two standard errors (SEs). Also see Model 3 (Table 2) for the reciprocal effectsrelating physical self-concept and exercise behavior. PSC � physical self-concept; EAtt � exercise attitudes; EInt � exercise intentions; EPBC � exerciseperceived behavioral control.* p � .05.

323PHYSICAL SELF-CONCEPT AND EXERCISE

as T1 exercise attitudes. This pattern of results is consistent withthe skill development model discussed earlier (see Figure 1A),suggesting that exercise attitudes are primarily a consequence ofother variables considered in the model, rather than a cause ofthem.

In summary, Model 9 provides clear support for the reciprocaleffects model of relations between physical self-concept and ex-ercise behavior but also provides reasonable support for the TOPB.Consistent with predictions from the reciprocal effects model,there were significant effects of T1 physical self-concept on T2exercise behavior and of T1 exercise behavior on T2 physicalself-concept. It is important to note that these effects were stillstatistically significant even after controlling for the additionalthree T1 variables based on the TOPB as well as the effects of sexand age. Consistent with the TOPB, the largest effect on T2exercise behavior (other than T1 exercise behavior) was T1 be-havior intentions. However, apparently inconsistent with the un-derlying rationale of the TOPB but clearly consistent with thereciprocal effects model, T1 physical self-concept had a direct

effect on T2 exercise behavior beyond effects that might have beenexplained in terms of TOPB variables.

Discussion

There now exists an extensive body of research in support of thereciprocal effects model of relations between academic self-concept and academic achievement in traditional academic set-tings. In the present investigation, consistent with academic self-concept research, we found clear support for the reciprocal effectsmodel: More positive levels of prior physical self-concept led tohigher subsequent levels of exercise behavior; higher levels ofprior exercise behavior led to higher levels of subsequent physicalself-concept.

Juxtaposition of TOPB and Reciprocal Effects Model

A potentially important contribution of the present investigationwas the juxtaposition of the TOPB and the reciprocal effects

Table 4Effects of Time 1 Predictor Variables on Time 2 Outcome Variables: Juxtaposition of Theory of Planned Behavior and ReciprocalEffects Model

Predictor (Time 1) variable

Time 2 outcome (dependent) variables

PSC EBeh EAtt EInt EPBC

� SE � SE � SE � SE � SE

Model 8Fixed effects

EBeh .28* .02 .07* .02 .14* .02 .14* .02EAtt .00 .02 .29* .02 .03 .02 �.01 .02EInt .16* .02 .08* .03 .27* .02 .16* .02EPBC .06* .02 .01 .03 .18* .02 .32* .02Age �.18* .02 �.04* .02 �.16* .02 �.22* .03Sex (male adolescents � �1,

female adolescents � 1) �.11* .02 .07* .02 �.03 .02 �.01 .02Age � Sex �.05* .02 �.01 .02 �.06* .02 �.05* .02

Residual varianceTeacher .02* .01 .00 .01 .00 .01 .01 .01Class .00 .01 .00 .01 .00 .01 .01 .01Students .73* .02 .87* .03 .67* .02 .60* .02

Model 9Fixed effects

PSC .35* .02 .08* .02 .01 .02 .08* .02 .10* .02EBeh .06* .02 .27* .02 .07* .02 .13* .02 .13* .02EAtt .01 .02 .00 .02 .29* .02 .03 .02 .01 .02EInt .02 .02 .15* .03 .09* .03 .26* .02 .15* .02EPBC .07* .02 .05 .03 .01 .03 .17* .02 .30* .02Age �.08* .02 �.16* .02 �.04* .02 �.15* .02 �.20* .03Sex (male adolescents � �1,

female adolescents � 1) �.16* .02 �.09* .02 .07* .02 �.02 .02 �.01 .02Age � Sex �.01 .02 �.05* .02 �.01 .02 �.05* .02 �.05* .02

Residual varianceTeacher .02* .01 .02* .01 .00 .01 .01 .01 .01 .01Class .01 .01 .00 .01 .00 .01 .00 .01 .01 .01Students .72* .02 .72* .02 .87* .03 .66* .02 .59* .02

Note. All outcome and predictor variables were standardized (M � 0, SD � 1), so that beta weights correspond to standardized beta weights. All betaweights are statistically significant when they differ from zero by more than two standard errors (SEs). PSC � physical self-concept; EBeh � exercisebehavior; EAtt � exercise attitudes; EInt � exercise intentions; EPBC � exercise perceived behavioral control.* p � .05.

324 MARSH, PAPAIOANNOU, AND THEODORAKIS

model. The results of the present investigation suggest that itwould be useful to supplement the TOPB with a measure ofphysical self-concept for studies of exercise behavior or, moregenerally, a domain-specific measure of self-concept that is logi-cally related to the target behavior in applications of the TOPB toother (nonphysical) domains. As suggested by Hagger et al.(2003), it may be appropriate to add new constructs to the TOPBif there is support for their discriminant validity in relation toexisting TOPB constructs. This proposal is supported in that phys-ical self-concept is well defined, is logically and theoreticallyrelated to exercise behavior and TOPB constructs, is clearly dis-tinct from other TOPB constructs (Marsh, Papaioannou, et al., inpress; Papaioannou et al., 2004), and contributes to the predictionof T2 exercise behavior and other TOPB constructs beyond thatwhich can be predicted by corresponding T1 measures.

It is, however, important also to emphasize several apparentlyimportant differences between the two approaches. First, in recip-rocal effects studies, self-concept researchers have focused on aclear temporal separation between the multiple waves. In theirgeneral recommendations, Marsh et al. (1999) suggested that thereshould be at least two waves of data but that three or more werepreferable and that the multiple waves should span at least 1 year.This focus on a multiwave design is consistent with the clearseparation between cause and effect emphasized in this research.In contrast, TOPB studies are frequently based on a single wave ofTOPB variables or, perhaps, a single wave of data augmented witha prior measure of behavior. In this respect, the causal ordering ofthe TOPB constructs is based on theory that is not readily ame-nable to empirical tests like those in the reciprocal effects studies.In particular, when the TOPB constructs are collected at more orless the same time, their temporal ordering cannot be used as thebasis of causal ordering. The multiwave strategy used in thereciprocal effects model might even be considered antithetical tothe TOPB studies for which it is sometimes suggested that the timegap between behavioral intentions and actual behavior should beminimized. A potential limitation of the reciprocal effects ap-proach, however, is that there is an implicit assumption that theeffects of self-concept on the desired outcome are mediated byintervening variables such as motivation, choice of behavior, ef-fort, persistence and, perhaps, variables like those posited in theTOPB.

Related to this issue of multiwave design is the focus on causalordering that is explicit and provides the basis of specific empiricaltests in the reciprocal effects model but implicit and largely un-tested in the TOPB (see Albarracin et al., 2001). Implicit in ourdiscussion is the assumption that the effects of physical self-concept on exercise behavior are either direct (unmediated) effectsor indirect effects that are mediated through the TOPB constructs.This implies a causal ordering in which physical self-conceptcomes before the TOPB constructs. It is important to note, how-ever, that this assumption was not made in any of our actualstatistical analyses (which only assumed that T1 variables camebefore T2 variables) and that there is no clear empirical basis ofsupport for such a causal ordering of variables within each wave ofdata. Although it might be reasonable—and consistent with theTOPB—to posit that the effects of physical self-concept are me-diated through behavioral intentions, results of Model 6 suggestthat the relations might be reciprocal such that behavioral inten-tions both influence and are influenced by physical self-concept.

Results of Model 9 suggest that there might be reciprocal relationsbetween intentions and behavior and between intentions and per-ceptions of control. Although the evaluation of the causal orderingamong TOPB constructs is beyond the scope of the present inves-tigation, it is important to reiterate that this fundamental concern ofthe reciprocal effects model is largely ignored in TOPB research,even though it has critical implications for the design of interven-tions to change exercise behavior.

Limitations and Speculations for Future Research

Outcome measures considered. Marsh (1997, 2002) arguedthat it is important for health, sport/exercise, and physical educa-tion researchers to focus on physical components of self-conceptrather than—or in addition to—global self-esteem and nonphysi-cal components of self-concept. Indirectly, the present investiga-tion supports this claim in showing that prior physical self-conceptcontributed to the prediction of subsequent exercise behavior be-yond the contribution of prior exercise behavior. Stronger testswould have been possible, however, if additional components ofself-concept had been included. For example, in support of theconvergent and discriminant validity of these effects, we speculatethat nonphysical components of self-concept would have had littleinfluence on subsequent exercise behavior, whereas the effects ofglobal self-esteem would have been much smaller than those forphysical self-concept. Furthermore, we suggest that the effect ofphysical self-concept would have been even greater for specificdomains of physical self-concept that were even more closelyaligned to the target exercise behavior than the global componentof physical self-concept considered in the present investigation.Thus, for example, each of the three physical self-concept instru-ments considered in Marsh, Richards, Johnson, Roche, andTremayne’s (1994) study included specific physical self-conceptscales that were more specifically related to exercise behavior andphysical activity than the corresponding global physical scalesfrom these instruments. Although pursuit of these speculations isclearly beyond the scope of the present investigation, this is animportant area for future research.

Because the present investigation was part of a large, nationallyrepresentative sample of Greek primary, junior high school, andsenior high school students designed to provide a census of diverseoutcomes relevant to all levels of physical education, there was aneed to measure each construct with a small number of items (seePapaioannou, 2000). However, psychometric support for most ofthe constructs considered here was good (Marsh, Papaioannou, etal., in press; also see Appendix). Problematic, however, was reli-ance on a single item to measure exercise behavior. Despitereasonable support of its construct validity and test–retest corre-lation and the common use of scales like this in other TOPBresearch, single-item scales are likely to be unreliable so thateffects presented here are likely to be smaller than those that wouldbe obtained if a more reliable, multi-item measure had been used.Whereas resolution of this problem is beyond the scope of thepresent investigation, it is important for future research in this areato incorporate stronger, more diverse measures of exercise behav-ior (e.g., Ainsworth, Montoye, & Leon, 1994; Jacobs, Ainsworth,Hartman, & Leon, 1993; Marsh & Johnson, 1994; Sirard & Pate,2001; Trost, 2001).

325PHYSICAL SELF-CONCEPT AND EXERCISE

Multilevel sampling design. The multilevel approach is animportant contribution of the present investigation, providing astatistically and substantively appropriate means to evaluate theeffects of particular teachers. Unless there are systematic differ-ences between teachers or classes on critical outcome variables,there is little basis for arguing that teachers make much differencein terms of these outcomes. In support of this approach, there wasstatistically significant variation among teachers on most of theconstructs considered in the present investigation. Important con-tributions of the present investigation were to demonstrate thatmost of the variation associated with individual teachers general-ized across different classes that were taught by the same teacher,supporting interpretations of these variance components as teachereffects. Similarly, it was important to demonstrate that variationamong teachers was still statistically significant on T2 outcomes,even after controlling for parallel outcomes collected at T1, andthat these differences also generalized across different classestaught by the same teacher (see Table 1).

The sampling design used in the present investigation was basedon only one teacher per school. Whereas this feature of thesampling design was important in obtaining a large, diverse, rep-resentative sample in relation to the number of teachers included inthe study, it precluded possibility of disentangling the effects ofindividual schools from the effects of teachers within the schools.Hence, future research should consider the use of sufficiently largesamples to include multiple teachers per school. Although wesuspect that effects of individual teachers within a school are moresubstantial than overall effects of schools, and there is support forthis conjecture in relation to academic achievement in traditionalschool subjects, we know of no relevant research on the effects ofphysical education teachers on physical activity. We note however,that whereas teacher effects at T1 or T2 considered separately areconfounded with school-level effects—if school-level effects ex-ist—changes over the school year are likely to be a function of theteacher rather than the school (because the effect of school is likelyto be relatively constant at T1 and T2).

The inevitable problems associated with missing data in longi-tudinal studies were compounded in the present investigation bythe necessity (for ethical reasons) to match students on the basis ofdemographic characteristics (e.g., birth dates) rather than actualnames. Whereas this problem may have compromised the nationalrepresentativeness of the data to some extent, we note that theanalyses are valid in relation to students who could be matched andthat the critical relation between T1 physical self-concept andbehavior was similar for students who could and could not to bematched with T2 responses.

Size of teacher effects. Teacher effects at T2, after controllingfor the matching T1 variable, were not large (e.g., intraclasscorrelations of .04 for physical self-concept and .09 for exercisebehavior; see Table 1) and were even smaller after controlling fordemographic and other outcome variables (e.g., residual variancecomponents of .02 and .04 in Model 2, Table 2). However, someimportant caveats need to be considered in the interpretation ofthese results. First, the criterion of teacher effects in the presentinvestigation (T2 effects at the end of the school year after con-trolling for T1 effects collected at the start of the school year aswell as effects that generalized over different classes taught by thesame teacher) is more demanding than that considered in mostother research of teacher effects in traditional academic settings

that are based on a single wave of data. Second, the estimates areconservative in that any teacher effects already experienced at T1were partialed out of T2 effects. Third, the main outcome variableconsidered here—exercise behavior outside of school—is qualita-tively different from the academic achievement within the schoolcontext used in most studies of teacher effectiveness (see discus-sion by Hagger et al., 2003). Hence, this outcome requires thatskills, competencies, knowledge, and values imparted to the stu-dent by a teacher in one context are transferred to another con-text—how students spend their time outside of school. Althoughwe agree with Hagger et al. (2003), Fox and Biddle (1988), andothers that physical education teachers are in a unique position toinfluence the long-term, health-related physical activity levels oftheir students, there is apparently little previous research to dem-onstrate that this actually occurs. Whereas the results of the presentinvestigation are theoretically important in demonstrating signifi-cant effects associated with individual teachers and are practicallyimportant in providing a clear methodological basis for how toevaluate this question, the practical implications may be limited bythe modest sizes of these effects. However, in an area where thereare apparently no methodologically defensible estimates of the sizeof teacher effects, accurate estimates of these effect sizes arerelevant, even if they are smaller than some might have hoped.Also, support for the reciprocal effects model that is the main focusof the present investigation does not depend on large differencesassociated with individual teachers. Indeed, the small teachereffects support the generality of support for the reciprocal effectsmodel across teachers. Nevertheless, although clearly beyond thescope of the present investigation, future research should seek toidentify strategies used by the most and least effective teachers tomaximize the effects associated with individual teachers.

Summary and Implications

Results of the present investigation have significant implicationsfor the importance placed on physical self-concept as a means offacilitating exercise and health-related physical activity as well asbeing an important outcome variable. If the direction of causalityhad been from self-concept to exercise behavior (self-enhancementmodel), then physical education teachers and health professionalsmight be justified in placing most of their effort into enhancingstudents’ self-concepts rather than fostering exercise per se. On theother hand, if the direction of causality had been from exercise toself-concept (skill development model), then they should focusprimarily on improving exercise levels, in that this is also the bestway to improve physical self-concept. In contrast to both theseapparently oversimplistic (either–or) models, the reciprocal effectsmodel implies that physical self-concept and exercise behavior arereciprocally related and mutually reinforcing. Improved physicalself-concepts will lead to improved exercise behavior, and im-proved exercise behavior will lead to better physical self-concepts.Hence, if physical education teachers and health professionalsenhance students’ physical self-concepts without improving exer-cise behavior, then the gains in self-concept are likely to be smallerand less long lasting. However, if physical education teachersimprove students’ exercise behavior without also fostering stu-dents’ self-beliefs in their physical capabilities, then the exercisegains are also likely to be smaller and less long lasting. If theyfocus on either one of these constructs to the exclusion of the other,

326 MARSH, PAPAIOANNOU, AND THEODORAKIS

then both are likely to suffer. Hence, according to the reciprocaleffects model, physical education teachers and health care profes-sionals should strive to improve simultaneously both physicalself-concept and exercise behavior.

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Appendix

Items Designed to Measure Five Constructs: Coefficient Alpha Estimates of Reliability andTest–Retest Correlations

Physical Self-Concept (T1, � � .80; T2, � � .82; rxx � .58)

Some people feel that they are good when it comes to playing sports.Some people feel that they are among the best when it comes to athletic

ability.Some people are quite confident when it comes to taking part in sports

activities.Some people feel that they are always one of the best when it comes to

joining in sports activities.Given the chance, some people are always one of the first to join in

sports activities.

Exercise Attitudes (T1, � � .58; T2, � � .75; rxx � .37)

Doing regular exercise in the next 12 months isvery good � 7, very bad � 1very healthy � 7, very unhealthy � 1very useful � 7, very useless � 1.

Exercise Perceived Behavioral Control (T1, � � .83; T2, � �.90; rxx � .64)

For me, doing regular exercise in the next 12 months is (very easy � 7,very difficult � 1).

I can exercise regularly in the next 12 months (very possible � 7, veryimpossible � 1).

I am absolutely certain that I will exercise regularly in the next 12months (absolutely right � 7, absolutely wrong � 1).

Exercise Intention (T1, � � .81; T2, � � .87; rxx � .58)

I intend to exercise regularly in the next 12 months (very possible � 7,very impossible �1).

I am determined to exercise regularly in the next 12 months (absolutelyyes � 7, absolutely no � 1).

Exercise Behavior (rxx � .41)

Exercise means taking part in physical activity for more than 30 min thatincreased your heart rate and caused sweating (e.g., football, basketball,aerobics). How many times did you exercise in the last month? None, 1–5,5–10, 10–15, 15–20, over 20.

Note. Time 1 (T1) � and Time 2 (T2) � refer to coefficient alpha estimatesof reliability at T1 and T2; rxx � test–retest correlation across the course ofthe school year. For present purposes, we conducted a confirmatory factoranalysis on responses to the 14 items designed to measure five constructsat T1; responses to the same 14 items collected at T2 indicated that the apriori factor structure provided an excellent fit to the data in relation totraditional guidelines (e.g., Tucker–Lewis Index � .986, root-mean-squareerror of approximation � .033; also see Marsh, Papaioannou, Martin, &Theodorakis, in press) and was the basis of test–retest correlations reportedhere.

328 MARSH, PAPAIOANNOU, AND THEODORAKIS