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BEHAVIOR MODIFICATION / April 2002Littell, Girvin / STAGES OF CHANGE

The stages of change proposed by Prochaska and DiClemente have been applied to changeefforts within and outside of formal treatment and in relation to virtually any problem behavior.This model has gained widespread popularity in health psychology and addictions and is beingused to guide interventions and allocate treatment resources in several fields. In this article, theauthors review 87 studies on the stages of change across problem behaviors. Research findingssuggest that the proposed stages are not mutually exclusive and that there is scant evidence ofsequential movement through discrete stages in studies of specific problem behaviors, such assmoking and substance abuse. Although the stage model may have considerable heuristic value,its practical utility is limited by concerns about the validity of stage assessments. The model’sunderlying concepts and alternative views of readiness for change are considered, along withdirections for future research.

Stages of Change

A Critique

JULIA H. LITTELLHEATHER GIRVIN

Bryn Mawr College

According to the transtheoretical model (Prochaska &DiClemente, 1984, 1986, 1992), behavioral change occurs in a seriesof discrete stages. Whether within or outside of formal treatment andin relation to virtually any problem behavior, the stages includeprecontemplation (not thinking about change), contemplation (think-ing about change), action (behavioral change), and maintenance.Stage status and movement between stages are thought to be influ-enced by (a) the perceived pros and cons of a problem behavior (and

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AUTHORS’NOTE: We thank Leslie B. Alexander, Jim Baumohl, Carolyn Needleman, WilliamW. Reynolds, and anonymous reviewers for helpful suggestions on an earlier version of this arti-cle. Reprint requests may be addressed to Julia H. Littell, Graduate School of Social Work andSocial Research, Bryn Mawr College, 300 Airdale Road, Bryn Mawr, PA 19010; e-mail:[email protected].

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the decision balance between them), (b) self-efficacy (i.e., confidencein one’s ability to change the problem behavior), (c) temptations torevert to the problem behavior, and (d) 10 “processes of change,”which are basic coping mechanisms used to modify a problem(Prochaska & DiClemente, 1984, p. 33).1 Here, we focus on the stagesof change, which are the central organizing construct of thetranstheoretical model (Martin, Velicer, & Fava, 1996), the portion ofthe model that has captured the imagination of many practitioners andscholars (Davidson, 1992), and the point of departure for stage-matched interventions in several fields.

The “stages were empirically discovered” (Prochaska & Velicer,1997, p. 11) in the early 1980s and have been the subject of hundredsof articles, several books, and more than 175 empirical studies since1990. Widely used in health psychology (Weinstein, Rothman, &Sutton, 1998) and in the study and treatment of addictions (K. B.Carey, Purnine, Maisto, & Carey, 1999; Davidson, 1992; Sutton,1996), the stages of change model also appears in the literature oncommunity-based mental health services (e.g., McConnaughy,Prochaska, & Velicer, 1983; Mcconnaughy, DiClemente, Prochaska, &Velicer, 1989; O’Hare, 1996a, 1996b), child welfare (DePanfilis,2000; Gelles, 1996, 2000), intimate partner violence (Begun,Weinstein, & Strodthoff, 1998), and organizational change (J. M.Prochaska, 2000). The development of this model has been likened toa Kuhnian paradigm shift (Orford, 1992; J. M. Prochaska, 2000), andreactions to the discovery of a new stage in the early 1990s were com-pared to “the awe that might accompany the discovery of a newplanet” (Stockwell, 1992, p. 831). The stages of change are thought tohave considerable heuristic value because they portray change asmore than a simple, one-step process (Gelles, 1996) and may promotea less pejorative view of people who are not ready for change and thosewho relapse (Davidson, 1992; Sutton, 1996). In several fields, themodel is being used to guide interventions and determine who getswhat kind of treatment. Stage-matched interventions are thought to bemore effective than traditional action-oriented treatment for addic-tions and other problem behaviors (Prochaska, 1995; Prochaska &Velicer, 1997).

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Critics suggest that, like all stage theories, this model oversimpli-fies the complexities of behavioral change by imposing artificial cate-gories on continuous processes (Bandura, 1997, 1998; Davidson,1992, 1998; Sutton, 1996). The debate continues over “whetherchange is best represented as a continuous process or by discretestages” (Prochaska & DiClemente, 1998, p. 39).

What does the empirical evidence suggest? Proponents of themodel claim strong empirical support for the stages of change across awide range of populations and problems (e.g., Prochaska,DiClemente, Velicer, & Rossi, 1992; Prochaska & Velicer, 1997;Prochaska, Velicer, et al., 1994; Velicer, Hughes, Fava, Prochaska, &DiClemente, 1995; Velicer, Rossi, Prochaska, & DiClemente, 1996).Much of this evidence comes from research conducted at the Univer-sity of Rhode Island Cancer Prevention Research Center. Others claimthat evidence for the stage model is weak and is more consistent withcontinuum models (Weinstein et al., 1998, p. 298). Thoughtfulreviews of this literature—pro and con—have considered portions ofthe available evidence, but findings are rarely discussed in detail.Sutton’s (1996) review included some of the early studies of the stagemodel (up to 1993), whereas Davidson (1998) emphasized studiesconducted outside of the Rhode Island group. Weinstein and col-leagues (1998) described the kind of empirical research needed to testa stage theory and gave some useful examples. Bandura’s (1997)appraisal was criticized for not dealing with the model’s empirical evi-dence (Prochaska & Velicer, 1997). To date, the most extensive reviewof research findings is provided by K. B. Carey and colleagues (1999),who examined evidence on the psychometric properties of instru-ments used to assess readiness to change substance abuse. To ourknowledge, there are no comprehensive reviews of empirical evidenceon the validity of the stages of change across problem behaviors.

In this article, we review the development of the stages of changemodel, stage measures, and mounting empirical evidence across arange of populations and problem behaviors. We focus on the con-struct validity of the stage model (not the entire transtheoreticalmodel). Then, we examine the heuristic value and practical utility ofthis model. Finally, we examine the model’s conceptual framework

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and alternative views of underlying constructs and relationshipsamong them.

DEVELOPMENT OF THESTAGES OF CHANGE MODEL

In the early 1980s, James Prochaska, Carlo DiClemente, and theircolleagues (e.g., DiClemente & Prochaska, 1982; Prochaska &DiClemente, 1983, 1984, 1986) began to develop the stage model ofbehavioral change. They drew on the work of Horn (1972, 1976, citedin DiClemente & Prochaska, 1982), who proposed four stages ofprogress in changing health-related behavior (contemplating change,deciding to change, short-term change, and long-term change) andProchaska’s (1979) analysis of the common elements of various sys-tems of psychotherapy. Prochaska and DiClemente’s early empiricalwork focused on processes people used to alter problem behavior overcertain “periods of change.” In a 1982 retrospective study of cigarettesmoking cessation, these time periods were termed the “decision tochange, active change, and maintenance” (DiClemente & Prochaska,1982, p. 134).

In a subsequent article, also on smoking cessation, Prochaska andDiClemente (1983) identified five stages of change: precontempla-tion, contemplation, action, maintenance, and relapse. Their initialassumption that change “involves movement through an invariantseries of stages” (Prochaska & DiClemente, 1984, p. 21) was illus-trated with a wheel, showing unidirectional, cyclical movementthrough the states. This was later modified to allow for backwardmovement in the stage sequence (Prochaska, 1995). After the mid-1980s, relapse was viewed as an example of backward movement(regression) rather than a separate stage. Later, the wheel was replacedwith an upward spiral pattern to illustrate cyclical movement andeventual progression through the stages of change (Prochaska,DiClemente, & Norcross, 1992).

In 1983, McConnaughy et al. developed a scale (now known as theUniversity of Rhode Island Change Assessment [URICA]) in whichstages were defined and measured somewhat differently than before:




In the precontemplation stage, the person does not believe he or shehas a problem or is not thinking seriously about change. In contempla-tion, the person thinks he or she has a problem and is thinking aboutchange. In the decision-making stage, the person has decided he or sheis ready to change, has committed himself or herself to changing, buthas not started working on the problem. In the action stage, the personis making changes in his or her overt behavior. In maintenance, theperson has already changed but may find it difficult to maintain thechanges. Evidence for the decision-making stage was not found in thisgroup’s initial empirical work with outpatients in psychotherapy(McConnaughy et al., 1983, 1989); hence, it was dropped (folded intocontemplation), and subsequent attention was focused on the remain-ing four stages.

By 1991, the group had identified a stage they called preparation.Like decision making, preparation is located between contemplationand action, but it is defined in terms of past and present behavior andfuture intentions. Although contemplation has been operationallydefined as seriously considering change within the next 6 months,preparation is defined as planning to change within 30 days and hav-ing made at least one 24-hour change attempt in the past year(DiClemente et al., 1991; Prochaska et al., 1994).

Following studies of cigarette smokers and psychotherapy patients,research on the stage model was extended to alcohol dependence (e.g.,DiClemente & Hughes, 1990) and other addictions and then to a widerange of behaviors, such as overeating, safe sexual practices, andphysical exercise (Prochaska et al., 1994). One might wonder whethercommon stages could be discernable across such a wide range ofbehaviors, but the “transtheoretical model was intended to be a gen-eral model of behavior change rather than being specific to a singlebehavior problem like smoking” (Prochaska & DiClemente, 1992,p. 201). Proponents claim that there is

strong support for the generalizability of three basic constructs of thetranstheoretical model: the stages of change, the pros and cons, and theintegration between the stages and these decisional balance variables.These constructs and the relationships between them hold for behav-iors differing on such dimensions as acquisition and cessation, addic-tive and nonaddictive, frequent and infrequent, legal and illegal, public




and private, and socially acceptable and less socially acceptable.(Prochaska, Velicier et al., 1994, p. 44)

The stages of change are considered to be an ordered sequence ofdiscrete states. Although stage status changes over time, at any givenmoment a person is assumed to be in a single stage; hence, the stagesare thought to be mutually exclusive (Martin et al., 1996, p. 69). Indi-viduals “pass through each stage” (Prochaska, DiClemente, Veliceret al., 1992, p. 825) in an orderly fashion. Although this progression isnot usually linear (people relapse and cycle through the stages morethan once), stage skipping is not expected (Prochaska, DiClemente,Velicerer et al., 1992).

EMPIRICAL EVIDENCE

Empirical support for the stage model should indicate that there are(a) a set of discrete (qualitatively different) states with (b) sequentialtransitions between them.2 To examine these basic assumptions andthe generalizability claim, we review empirical studies of stages ofchange across a variety of problem behaviors and samples. We alsoexamine claims for the model’s predictive validity and its utility inmatching subjects to different types of treatment.

Through searches of PsychINFO, PsychNet, Social WorkAbstracts, Lexis-Nexis (General Medical and Health Topics),InfoTrac, and PubMed (Medline), we identified more than 175 empir-ical studies related to the transtheoretical model. Of these, 87 providedevidence on the stages of change; the remainder focused on other keyconstructs (the decision balance, self-efficacy, temptations, and pro-cesses of change) or provided insufficient information on stageassessments and are not included in our review. As described in theappendix, most of the studies concern smoking cessation, drug andalcohol problems, or adult mental health problems. We also found rel-evant studies located within clinical drug trials involving persons withanxiety disorders and in research on exercise acquisition, nutrition,weight control, skin cancer prevention (via sunscreen use), HIV riskreduction, adolescent delinquency, and the management of diabetesmellitus. Although most studies focused on problem reduction, some




investigators used the stage model to describe the acquisition of prob-lem behaviors, such as tobacco use.

Most studies of the stages of change have been cross-sectional,although longitudinal evidence is beginning to appear, particularly inareas related to cancer prevention (smoking cessation and sunscreenuse) and substance abuse. Convenience samples (clinic attendees andvolunteers) were used in all but a few studies. Sample sizes vary, butmost published reports involve more than 200 participants and severalinclude well more than 1,000.

STAGE OF CHANGE (SOC) MEASURESAND THEIR PROPERTIES

SOC ALGORITHMS

SOC is most commonly assessed with an algorithm (set of decisionrules) based on yes or no answers to a few questions about currentbehavior, future intentions, and (in some studies) past attempts tochange. For example, in most of the studies summarized by Prochaskaet al. (1994), participants were asked whether they were currentlyengaged in the problem behavior, were considering change within thenext 6 months, were considering change within the next 30 days, orhad made a 24-hour change attempt within the past year and, if notcurrently engaged in the behavior, how long it had been since theystopped. Answers were used to classify participants into five stagesusing the criteria shown in Table 1.

As shown in the appendix, algorithm questions and stage criteriaare not consistent across studies that use this approach. Some investi-gators use one statement to represent each stage and instructed partici-pants to select the statement that best describes them (e.g., Lerner,1990; Nigg et al., 1999; Stevens & Estrada, 1996). Some studies donot include questions about past attempts to change. Various timeframes are used as reference points, and any shift in these time frameswill alter the distribution of people across stages (Weinstein et al.,1998, p. 293). Although stage categories are most often representedby a set of mutually exclusive and exhaustive dichotomous variables,some investigators have used algorithms to create ordinal stage-of-





TABLE 1Comparison of Two Stage Measures: Algorithm and Scale

Stage Algorithm Criteriaa Selected URICA Itemsb

Precontemplation Problem behavior present As far as I’m concerned, I don’t have anyNot planning to change problems that need changing.within 6 months I may be part of the problem, but I don’t

really think I am.All this talk about psychology is boring.Why can’t people just forget about theirproblems?

I would rather cope with my faults than tryto change them.

Contemplation Problem behavior present It might be worthwhile to work on myConsidering change problem.within the next 6 months I’ve been thinking that I might want to

change something about myself.I have a problem and I really think I shouldwork on it.

I wish I had more ideas on how to solve myproblem.

Preparation Problem behavior present Not applicableConsidering changewithin the next month

Made a 24-hour changeattempt in the last year

Action Problem behavior absent At times my problem is difficult, but I’mChange occurred working on it.within the past 6 months Even though I’m not always successful in

changing, I am at least working on myproblem.

I have started working on my problemsbut I would like help.

Anyone can talk about changing; I’mactually doing something about it.

Maintenance Problem behavior absent I have been successful in working on myChange occurred more problem, but I’m not sure I can keep upthan 6 months ago the effort on my own.

I’m not following through with what I hadalready changed as well as I had hoped,and I’m here to prevent a relapse of theproblem.

I thought once I had resolved the problemI would be free of it, but sometimes I stillfind myself struggling with it.

I may need a boost right now to help memaintain the changes I’ve already made.

NOTE: URICA = University of Rhode Island Change Assessment.a. Prochaska et al. (1994).b. Rated on a 5-point scale from strongly disagree to strongly agree (McConnaughy,DiClemente, Prochaska, and Velicer (1989).



change variables (e.g., Hedeker & Mermelstein, 1998; Hedeker,Mermelstein, & Weeks, 1999; Morera et al., 1998). Recently, sixstages of change were combined with three stages of acquisition tocreate a continuum of nine stages of adolescent smoking (Pallonen,Prochaska, Velicer, Prokhorov, & Smith, 1998); although described incontinuous terms, the stages were treated as discrete entities. K. B.Carey and colleagues (1999) noted that due to variability in imple-mentation of algorithms, these measures are unstandardized and diffi-cult to evaluate psychometrically.

SOC SCALES

Some studies have used the URICA instrument or one of its vari-ants to assess SOC.3 The URICA includes four scales, thought to rep-resent precontemplation, contemplation, action, and maintenance;sample items are shown in Table 1. Several short forms and otherinstruments have been derived from or are closely related to theURICA. Modifications in wording are often made to fit specificbehavioral problems (e.g., “drinking problem”). However, in someversions of the URICA (particularly those used in mental health set-tings), the problem is not defined. The lack of a clear referent may beconfusing to respondents, particularly since the term problem appearsin both its singular and plural forms.

Jefferson (1991) noted several other problems in the constructionof the URICA, most of which apply to other SOC scales as well: (a)All items in each scale are scored in the same direction, whichincreases the likelihood of response sets; (b) 13 of the 32 items aredouble-barreled; (c) many items are worded using negatives “whichviolates an accepted principle of test construction. To respond in thenegative requires formulation of a double negative, which can be con-fusing to the reader” (p. 121); (d) several items are awkwardly wordedand difficult to answer on first reading (e.g., “I’m not followingthrough with what I had already changed as well as I had hoped, andI’m here to prevent a relapse of the problem”); and (e) some phrasesare not in common use (e.g., need for a “boost” to maintain previousgains). Davidson (1998) observed that there is semantic overlapamong items (which will inflate measures of internal consistency).




Furthermore, the URICA was normed on middle-class, Caucasianparticipants and may not perform well in other samples (Hutchison,1996; Jefferson, 1991).

Given these problems, SOC scales (the URICA and its variants)have somewhat uneven levels of internal consistency across samples.Some studies reported Chronbach’s alphas between .7 and .9 (Cady,Winters, Jordan, Solberg, & Stinchfield, 1996; Carney & Kivlahan,1995; Elder et al., 1990; Hilburger, 1995; Jefferson, 1991;McConnaughy et al., 1983, 1989; O’Hare, 1996b; Rollnick, Heather,Gold, & Hall, 1992), whereas others found one or more alphas lessthan .7 (Abellanas & McLellan, 1993; Belding, Iguchi, & Lamb,1996; Costa, 1990; DiClemente & Hughes, 1990; Lamb, Belding, &Festinger, 1995; Lerner, 1990; Rosenbloom, 1991; Suris, del CarmenTrapp, DiClemente, & Cousins, 1998; Tsoh, 1995).

Several methods have been used to identify stages based on SOCscales. Some studies have classified cases based on the highest rawscore (e.g., Franko, 1997; Hutchison, 1996; Rollnick et al., 1992;Trigwell, Grant, & House, 1997; Tsoh, 1995) or standardized score(Rollnick et al., 1992; Tsoh, 1995). Tied scores have been handled inseveral ways: by placing the participant in the more advanced stage(Rollnick et al., 1992), using ties to designate a separate stage (Smith,Subich, & Kalodner, 1995), or a combination of these two approaches(Heather, Rollnick, & Bell, 1993). These classification schemes aresomewhat arbitrary, and different classification methods producequite different results. For example, Tsoh (1995) reported thatdepending on the method used, between 0.3% and 26% of cases in hersample would have been placed in precontemplation. Hutchison(1996) found that 13% to 43% of cases could not be assigned to a pre-dominate stage, depending on the classification methods used.Because contemplation, action, and maintenance scores were corre-lated, Abellanas and McLellan (1993) found it very difficult to deter-mine the stages of change for any individual using URICA scores.

Some investigators have created continuous measures of readiness(or motivation) for change by reverse scoring the precontemplationitems and computing a total score. There is some empirical support forthis approach (Budd & Rollnick, 1996; Carbonari, DiClemente,Addy, & Pollak, 1996; Hutchison, 1996; Project MATCH Research




Group, 1997; Tsoh, 1995; Velasquez, Carbonari, & DiClemente,1999).

A COMPARISON OF ALGORITHMS AND SCALES

Staging algorithms have the practical advantage of being simpleand short, whereas the URICA instrument “is longer but has theadvantage of being more subtle and less susceptible to misreporting incontexts . . . where people may feel pressured to report that they aremore prepared to take action” (Prochaska & DiClemente, 1998, p. 40).Although it is assumed that either approach is appropriate for measur-ing the SOCs (Martin et al., 1996; Norman, Velicer, Fava, &Prochaska, 1998; Prochaska, DiClemente, Velicer et al., 1992), thealgorithms and SOC scales tap very different constructs. As shown inTable 1, none of the algorithm criteria (current behavior, future inten-tions, past attempts to change, and time frames) are used in the SOCscales. The scales focus on problem denial or admission, thoughtsabout change (not future intentions), working on problems (not actualchange), and concerns about relapse.

Very few published studies have compared stages derived from analgorithm with those obtained using SOC scales, but available evi-dence suggests that there is little concordance between them. Twostudies of tobacco use acquisition found agreement on precontemplationbut not on decision making (a combination of contemplation andaction) or maintenance (Elder et al., 1990; Stern, Prochaska, Velicer,& Elder, 1987). In contrast, Belding et al. (1996) found that contem-plation and action scales tended to match corresponding stagesderived from an algorithm, whereas there was less concordance onprecontemplation and maintenance. Finally, Lerner (1990) foundagreement only on precontemplation.

In sum, stage criteria and classifications are not consistent within orbetween the SOC algorithms and scales. Because multiple indicatorsare generally preferable to single measures of a construct (e.g., stage),stage scales should be stronger than algorithms; yet, as noted above,many of the scale items are not well constructed. With these concernsin mind, we examine available evidence for the stages of change.




EVIDENCE OF DISCRETE STATES

Although longitudinal data are needed to test any stage theory, afirst step is to confirm the existence of stages themselves. If the pro-posed stages are valid, they should appear as discrete (qualitativelydifferent) states in cross-sectional analyses. There might be somecases in transition between two adjacent stages, but there should be nooverlap between nonadjacent stages. Because algorithms place partic-ipants in mutually exclusive stage categories a priori, this precludesanalysis of potential overlap between stages. SOC scales are bettersuited for this purpose because they provide separate assessments ofeach stage. Several kinds of analysis have been performed to test thevalidity of the stage categories.

PRINCIPAL COMPONENTS AND FACTOR ANALYSIS

Some investigators have used principal components or factor anal-ysis to identify underlying dimensions of SOC measures. If distinct,the stages should emerge as separate components or factors in thesestudies. Initially, McConnaughy et al. (1983) identified four principalcomponents that they termed precontemplation, contemplation,action, and maintenance. Although some studies generally confirmedthis four-stage structure (Carney & Kivlahan, 1995; DiClemente &Hughes, 1990; Hilburger, 1995; Lerner, 1990; McConnaughy et al.,1989; O’Hare, 1996b; Rollnick et al., 1992), typically some of theexpected factor loadings were weak, and a few items loaded on the“wrong” component or factor.4 Other studies do not support thefour-stage structure. Precontemplation usually emerges as a distinctfactor, but other factors appear to represent combinations of contem-plation and action (Elder et al., 1990; Lamb et al., 1995; Stern et al.,1987), action and maintenance (Isenhart, 1994; Miller & Tonigan,1996), and contemplation and maintenance (Belding, 1993; Beldinget al., 1996). Rosenbloom (1991) found two contemplation compo-nents in her four- and five-component solutions. Hutchison’s (1996)four components appeared to represent affective states and copingmechanisms (not stages); she referred to these components asapproval seeking, fear of relapse, uncertain denial, and angry denial.




It is important to note that “support for the underlying theoreticalmodel of the stages of change” (Elder et al., 1990, p. 453) has beenbased on factor analytic solutions in which key stages could not be dis-tinguished from previous or subsequent stages, solutions that wereforced to yield a certain number of factors (Cady et al., 1996), andsolutions developed after dropping items that did not load on any fac-tor or loaded on more than one factor (31% to 50% of 32 items weredropped by Costa, 1990; Hilburger, 1995; Lerner, 1990; and Tsoh,1995). Other claims of support for the model come from studies thatimposed the four-stage model on the data after principal componentsanalysis “did not yield promising results” (Rosenbloom, 1991, p. 31).

Another approach has been taken by developers of the Stages ofChange Readiness and Treatment Eagerness Scale (SOCRATES),who concluded that “this instrument does not appear to measure thestage constructs. . . . Rather the scales of SOCRATES seem betterunderstood as continuously distributed motivational processes thatmay underlie stages of change” (Miller & Tonigan, 1996, p. 84). Threeinterpretable factors are used as continuous variables; they are as fol-lows: taking steps (a combination of action and maintenance items),recognition (precontemplation versus determination), and ambiva-lence (Miller & Tonigan, 1996).5

Hierarchical factor models have shown that a single underlying fac-tor may fit the data better than a stage model. Tsoh (1995) reportedthat a single, second-order factor accounted for the variance in fourfirst-order factors (which represented the URICA scales);Chronbach’s alpha for the total score was .79. Budd and Rollnick(1996) found similar results using the Readiness to Change Question-naire (Chronbach’s alpha = .85). Using another approach, Satterfield,Buelow, Lyddon, and Johnson (1995) found that all four URICAscales loaded on one canonical variable, with precontemplation load-ing in the opposite direction from the other scales.

COMPARISON OF SUBSCALESCORES AND CLUSTER ANALYSIS

If the stages are discrete, most people should have high scores onone SOC scale and low scores on the others. Rollnick et al. (1992)




found that this “strict interpretation” of the stage model held for only40% of their participants; 45% had high raw scores on two scales andlow scores on a third, and 14% had either high or low scores on allthree scales. Similar findings were reported by Heather et al. (1993),who also identified several “illogical” patterns, including (a) highscores on both Precontemplation and Action scales and (b) low scoreson all scales (15% of participants were in one of the illogical groupsand were excluded from further analysis).

The reality is that individuals who are at any point in the process ofchange can experience many of the attitudes in the URICA subscales atthe same time. That is why we have used cluster analysis rather thanthe more simplistic approach of classifying individuals solely basedon which subscale score is highest. (Prochaska & DiClemente, 1998,p. 40)

This approach seeks to identify natural (similar) groups of cases,based on their scores on all four SOC scales. To support the stagemodel, cluster analysis should yield groups that represent the stages,perhaps with a few groups in transition between adjacent stages.

McConnaughy et al. (1983) first presented an 18-cluster solution,but half of the clusters were uninterpretable; of the 9 interpretableclusters, only one group (3% of participants) had above average scoreson a single scale (Precontemplation). Their 1989 replication yieldedeight clusters, some of which were similar to those found in 1983; twoof the eight clusters (20% of participants) had high scores on only onescale (Precontemplation, again) and low scores on all others.DiClemente and Hughes (1990) identified five clusters: an “ambiva-lent” group with high scores on all four scales, an “uninvolved/dis-couraged” group with low scores on all scales, a “participation” clus-ter with high scores on three of four scales, a “contemplation” groupwith above-average scores on maintenance items as well, and onegroup (the precontemplation cluster, 28%) with high scores on onescale and below average scores on all others. In O’Hare’s (1996b)five-cluster solution, only the precontemplation group (10% of cases)was truly distinct; “contemplators” had virtually identical means oncontemplation and maintenance scales; “participators” had almostequal means on contemplation and action; “maintainers” had similarmeans on the Contemplation, Action, and Maintenance scales; and




fully 42% of the participants were in a cluster called “uninvolved,”with approximately equal means on all four scales. Similarly,Hilburger’s (1995) precontemplation cluster (27%) had high scores onthat scale only, the “contemplation/preparation” group had aboveaverage Contemplation and Action scores, the “action” cluster hadhigh scores on all scales except Precontemplation (their highest meanscore was on Contemplation), and the “maintenance group” had aboveaverage scores on all four scales. Beitman et al. (1994) found threeclusters, characterized by (a) high Precontemplation scores (29%), (b)low Precontemplation scores and high scores on all other scales, and(c) average scores on all four scales. Willoughby and Edens (1996)found that a two-cluster solution fit their data best; participants in onecluster (31%) had high scores on the Precontemplation scale; those inthe other had high scores on Contemplation and/or Action.

The results of studies that used cluster analysis have been charac-terized in several ways. Hilburger (1995, p. 68) and O’Hare (1996b,p. 17) concluded that their results support the stage model, althoughmost of their clusters (and participants) had roughly equivalent meanson two or more stage subscales. Davidson’s (1998) review indicatedthat “clear profiles corresponding to the predicted stages haveemerged with considerable consistency” (p. 27), whereas K. B. Careyand her colleagues (1999) said that “the number of identifiable clus-ters is sample dependent and highly variable” and that some clusters“do not have clear correlates in the Transtheoretical Model” (p. 251).In our view, these studies show that most participants are not in a sin-gle stage. Precontemplation clusters have been identified, but none ofthe other stages emerged as distinct states.

SUMMARY

In sum, empirical evidence indicates that the proposed SOCs arenot discrete. Participants can be placed in mutually exclusive catego-ries via algorithms, but as others have noted, these distinctions may beartificial. Analyses of SOC scales indicate that most people agree withitems that are thought to reflect different stages. Some participantsendorse items that reflect nonadjacent stages. With the exception ofprecontemplation, the stages do not emerge in any consistent manner




in principal components, factor, or cluster analysis across or withinproblem behaviors. An observation made in 1983 still holds truetoday: “Rather than simply being in one stage or another, clients showpatterns of differential involvement in each of the stages”(McConnaughy et al., 1983, p. 374). Sutton (1996) observed that ifone can be in more than one stage at once, “the concept of stages losesits meaning” (p. 195).

Most of the recent work in this area is based on the assumption thatdiscrete stages exist and have already been empirically validated (e.g.,Martin et al., 1996, p. 69; Velicer et al., 1996, p. 558). Many research-ers have moved on to attempts to examine and explain movementbetween stages.

EVIDENCE OF SEQUENTIAL TRANSITIONS

The stage model suggests that there are sequential transitionsbetween qualitatively different states in the change process. Althoughbackward movement (regression) is permitted, people are thought topass through each stage as they progress (Prochaska, DiClemente,Velicer et al., 1992). Stage skipping is not expected. The model doesnot specify how long it takes to move through the stages, but the notionof cyclical progression suggests that this varies.

CROSS-SECTIONAL EVIDENCE

To confirm the proposed stage sequence, correlations among SOCscales should indicate that adjacent stages are more closely relatedthan nonadjacent stages (McConnaughy et al., 1989). This “simplexpattern” was found in some studies (e.g., McConnaughy et al., 1983;Rollnick et al., 1992) but has not been replicated in larger samples. In1989, McConnaughy and colleagues reported a moderate correlationbetween two nonadjacent stages, which was similar to correlations foradjacent stages.6 Hence, their often-cited conclusion that results“demonstrate this simplex pattern” (McConnaughy et al., 1989, p. 501)is not entirely convincing. Others have found higher correlationsbetween nonadjacent stages (contemplation and maintenance) than




adjacent ones (Abellanas & McLellan, 1993; Belding, 1993; Beldinget al., 1996; Hilburger, 1995; Tsoh, 1995).

Cross-sectional data have been used to make unwarranted infer-ences about movement from one stage to another. For example, in across-sectional study, participants with high Action scores wereviewed as “more likely . . . to have progressed from theprecontemplation to contemplation stage . . . through the determina-tion stage. . . . to the action stage” (Farabee, Nelson, & Spence, 1993,p. 343). Using cross-sectional data from a study on skin cancer pre-vention, Hedeker et al. (1999) conducted an analysis of predictorsassociated with crossing different “thresholds of change” (i.e., move-ment from precontemplation to contemplation and from contempla-tion to action). Inferences about movement and thresholds were basedon point-in-time comparisons between separate groups constructedwith an algorithm, although differences between these groups mayhave nothing to do with change over time.

LONGITUDINAL EVIDENCE

All of the longitudinal studies we found used algorithms to classifyparticipants into stages. Several studies described stability and move-ment between stages over time; others identified factors associatedwith stability or movement.

Stability and change. Several prospective studies of smokers havedemonstrated some stability in stage classifications over 2 years, moreprogression than regression, and little stage skipping (Herzog,Abrams, Emmons, Linnan, & Shadel, 1999; Morera et al., 1998; Nor-man et al., 1998; Pierce, Farkas, & Gilpin, 1998). In the study reportedby Norman and colleagues (1998), 2,088 participants were classifiedin precontemplation, contemplation, or preparation at baseline.Twenty-one percent remained in their baseline stage in each of foursubsequent observations over 2 years, 23% began and ended in thesame stage with various ups and/or downs in between, 39% ended upin a higher stage, and 17% regressed to an earlier stage. However,there were 446 different patterns of stage classifications over fivepoints in time; only 37 of these patterns were exhibited by more than




10 participants. Ten mutually exclusive and exhaustive groups(dynatypes) were created for further analysis; 2 of these weremonothetic and stable (in precontemplation or contemplation) acrossall observation points, and the remaining 8 were polythetic, defined bybaseline and end stage only. Although this study “revealed distinctlongitudinal patterns of change” (Norman et al., 1998, p. 149) basedon the stages of change, 80% (1,668) of the participants were ingroups that were heterogeneous with respect to stage classificationson at least three of the five observations.

Almost half (977) of all participants were in progressing or regress-ing groups in which several end stages were possible;7 baseline stagewas the only stage classification these participants shared with allother members of their dynatype.

Martin et al. (1996) used latent transition analysis (LTA) to modelmovement between stages over five observation points. Three LTAmodels were tried: The first only allowed forward movement, the sec-ond allowed one-stage forward and one-stage backward movement,and the third permitted forward movement by one or two stages andbackward movement by one stage only. Of the three, the two steps for-ward, one step back model fit the data best and was used to estimateeach of the four possible transitions that may have occurred betweenobservations. Overall, the proportion of participants in precontemplationand contemplation decreased over time, whereas the proportion inmaintenance increased. In each of the transition models, the actionstage was the least stable (i.e., participants in action were likely to bein another stage at the next observation point). The maintenance stagewas the most stable, but that may be because the model restrictedmovement out of maintenance (forward movement from maintenancewas not possible, and backward movement was restricted to onestage). Martin et al. noted that “the probability of transition to adja-cent stages was greater than nonadjacent stages” (p. 76) and “pro-gression between stages is more likely than regression” (p. 78), butthese patterns may be artifacts of limitations imposed on stage skip-ping and backward movement. Similarly, Hedeker and Mermelstein(1998) developed a longitudinal “thresholds of change” model, butthey only considered stage stability and forward movement.




Correlates of stability and change. Here, we focus on the mostwidely cited longitudinal study, which has served as the basis for sev-eral important reports. In this study, data were collected from smokersat 6-month intervals over a 2-year period. Initial reports examined sta-bility or movement between stages in relation to the processes ofchange, decision balance variables (the pros and cons of smoking),self-efficacy, and temptations to smoke (Prochaska, DiClemente,Velicer, Gilpin, & Norcross, 1985) or in relation to the presence ofhealth problems, habit strength, and socioeconomic status (Wilcox,Prochaska, Velicer, & DiClemente, 1985). Based on data from all fiveobservation points, researchers identified 14 patterns of stability ormovement over the 2-year period (Prochaska, Velicer, Guadagnoli, &Rossi, 1991). There was some stage skipping, although it is possiblethat passage through intermediate stages occurred between data col-lection points. Fifty (5% of 960) participants had idiosyncratic pat-terns that did not fit any of the 14 subgroups. Data from 6 of the 14groups—representing 27% of the sample (260 cases)—were used toillustrate patterns of change. The selected groups were those who (a)remained in precontemplation, (b) moved from precontemplation tocontemplation, (c) moved from contemplation to action, (d) remainedin action, (e) moved from action to maintenance, and (f) remained inmaintenance for 2 years. The selected groups represented ideal pat-terns: stability within a stage or forward progression from one stage tothe next. The researchers also found V patterns (regression followedby progress), inverted Vs (progress then regression), various N pat-terns (ups, downs, and ups), stage skipping, and other less-than-idealtypes of movement during the 2-year observation period. Thesenot-so-neat cases were excluded from illustrations of change overtime. The authors stated that “meaningful patterns could not be dis-cerned until the change processes were organized across groups repre-senting different stages of change” (Prochaska et al., 1991, p. 101).

In several striking graphs, the six selected groups are arrayed fromleft to right on the horizontal axis in the order above (fromprecontemplation to maintenance). Within each group, data from fiveobservation points are plotted in chronological order. This gives theappearance of 30 separate observations. Lines connecting these 30




data points appear to represent movement over the entire stagesequence. Consistent with the transtheoretical model, it appears thatthe pros (perceived advantages) of smoking decrease fromprecontemplation to maintenance, whereas the cons (perceived disad-vantages) increase from precontemplation to contemplation and thengradually decrease from action onward. Temptation scores are highand self-efficacy scores low from precontemplation to contemplation,then self-efficacy scores increase and temptation scores decrease fromaction onward.

The basic pattern of change processes across stages of change can bestbe represented by a mountain metaphor. The change processes fol-lowed a general curvilinear pattern of climbing from precontemplationto contemplation, peaking at a particular stage of change, and thendescending either to precontemplation levels or to somewhat higherlevels if used as relapse prevention strategies during the maintenancephase. (Prochaska et al., 1991, p. 102)

Recall that none of the participants in this analysis progressed throughthe whole stage sequence. Rather, these patterns were obtained byarraying longitudinal data from six selected subgroups, each of whichrepresents stability within a stage or one-stage forward movementover a 2-year period. The exclusion of almost three quarters of thecases and combination of cross-sectional and longitudinal data createthe appearance of a stage sequence, which may not actually be foundin the data.

Using data from the same longitudinal study, Velicer et al. (1996)examined the direction of influence between cognitive and behavioralmeasures (i.e., whether cognition influenced subsequent behavior orvice versa). As they expected, reductions in habit strength (number ofcigarettes smoked per day and score on a scale of temptations tosmoke) preceded reductions in the perceived pros of smoking. Theresearchers expected (a) increases in perceived cons of smoking toprecede changes in behavior (reductions in habit strength) as partici-pants moved from precontemplation to contemplation and (b)changes in behavior (decreased habit strength) to precede decreases incons during movement from contemplation to maintenance. Becausethere were too few participants in the precontemplation or contempla-tion stages in all five waves, the first part of this hypothesis could not




be tested. There was evidence that behavioral change preceded reduc-tions in the cons of smoking in the later stages. In other words, behav-ioral change precedes some cognitive changes (the perceived pros andcons of smoking). This is somewhat at odds with the basicthink-before-acting progression of the stage model. In a separate,2-year prospective study of smokers, Herzog et al. (1999) found thatthe pros, cons, and processes of change were not predictive of progres-sive movement between stages.

Pollak, Carbonari, DiClemente, Niemann, and Mullen (1998)looked at the direction of causality in relationships between decisionbalance and process variables within several stages of change. Theauthors concluded that this “pattern of relationships resemblesexpected progression through the stages” (p. 446). They argued thatthe stage-specific relationships between the decision balance and pro-cesses of change “support the position that there are distinct stages ofchange rather than a single continuum” (p. 447). To our knowledge,relationships between continuous measures of readiness for changeand other concepts from the transtheoretical model have not beenexamined.

Norman and colleagues (1998) analyzed data on a representativesample of 2,088 smokers to determine whether “those who madeprogress [were] more motivationally ready to change at the baselineassessment” (p. 143). Dynatype groups (described above) with thesame baseline stage were compared on 2 decision balance scales, 3temptations subscales, 10 processes of change scales, and 2 behav-ioral measures of habit strength. Statistically significant differencesbetween group means were found on some scales (precontemplationdynatypes differed on 5 of the 17 scales, contemplation dynatypes on4, preparation on 7). However, most of these differences were verysmall (less than 3 points using standardized T scores) and accountedfor no more than 3% of the variance. Usually a progressing or regress-ing dynatype group differed from other groups with the same baselinestage. For example, among groups that began in precontemplation,those who remained in that stage throughout the study report signifi-cantly fewer cons of smoking at baseline (mean = 46.1) than thosewho ended up in a higher stage (mean = 48.8); the third, “vacillating,”group was not significantly different from the “stable” or “progress-




ing” precontemplation groups on this scale (mean = 47.9). Hadprecontemplation or readiness for change been treated as continuousvariables, motivational differences among precontemplators mighthave been identified at baseline (e.g., stable precontemplators mighthave had higher baseline precontemplation scores than those that vac-illated or progressed). The authors concluded that the dynatypegroups were validated using variables from the transtheoretical model(Norman et al., 1998, p. 150).

Using only the baseline and 1-year follow-up observations from thesame data set, Velicer, Norman, Fava, and Prochaska (1999) com-pared groups that started in the same baseline stage and eitherremained at that stage, progressed, or (if possible) regressed. Theyfound some differences between groups on decision balance or temp-tation scales. Most hypotheses were supported. Again, it appears thatthere may be motivational differences among participants who sharethe same baseline stage assessment.

SUMMARY

In sum, there is little empirical evidence of sequential transitionsbetween stages. Longitudinal studies have used single-stage classifi-cations at each observation point. More than 400 patterns of stabilityand movement between stages have been reported (Norman et al.,1998), but no studies have documented movement through the entirestage sequence. Associations between stage classifications and othervariables have been reported, but it is not clear whether these might bebetter accounted for by continuous measures of readiness for change.

PRACTICAL UTILITY

“Across a variety of problems and populations [the] first threestages have been practical predictors of who signs up for health pro-motion programs, who shows up, who finishes up, and who ends upbetter off” (Prochaska & Velicer, 1997, p. 11). For example,precontemplators are expected to be less likely than others to sign up,finish up, and succeed in treatment. The evidence for this is mixed.




Some studies found no significant relationship between SOCs andmeasures of treatment attendance, duration, or program completion(e.g., Cady et al., 1996; Hutchison, 1996; Isenhart, 1994; Kavanagh,Sitharthan, & Sayer, 1996; Lamb et al., 1995; Willoughby & Edens,1996). In others, high precontemplation scores were predictive ofdropout (Smith et al., 1995), but sometimes in the “wrong” direction(i.e., longer retention in treatment) (Belding, Iguchi, Lamb, Lakin, &Terry, 1995; Jefferson, 1991). Prochaska, Velicer, Fava, Rossi, andTsoh (1996, as cited in Norman et al., 1998) found no evidence of dif-ferential attrition by baseline stage. Similarly, baseline stage has beenpredictive of outcomes in some studies (e.g., Beitman et al., 1994;Crittenden, Manfredi, Warnecke, Cho, & Parsons, 1998; DiClementeet al., 1991; McConnaughy, 1984; Wilson, Bell-Dolan, & Beitman,1997) and not others (Farkas, Pierce, Gilpin, et al., 1996; Farkas,Pierce, Zhu, et al., 1996), and there are mixed results within studies(Belding, Iguchi, & Lamb, 1997; Heather et al., 1993; Isenhart, 1997;Kavanagh et al., 1996; Reid, Nair, Mistry, & Beitman, 1996; Tsoh,1995). Using a single motivation score derived from a subset ofURICA items, the Project MATCH Research Group (1997) found thatinitial motivation for change was predictive of better outcomes amongoutpatients but not those in aftercare treatment for alcoholism. In theirreview of prospective studies of stages of change, Belding et al. (1997)concluded that “none clearly and consistently supports the predictivevalidity of the model” (p. 65).

Farkas, Pierce, Zhu, et al. (1996) suggested that stage of changealgorithms may lack predictive power because they include informa-tion on future intentions, which are not as accurate as behavioral mea-sures in predicting outcomes. The social-psychological literatureindicates that intentions and behavior are not closely related and havedifferent determinants (Miller, 1985). Hence, the stages model has,for example, “led to smokers with markedly different probabilities offuture success being placed at the same stage” (Pierce et al., 1998,p. 282).

There has been much discussion of the promise of stage-matchedinterventions (e.g., Pierce et al., 1998; Prochaska, 1995; Prochaska,DiClemente, & Norcross, 1992; Prochaska, DiClemente, Velicer etal., 1992; Prochaska & Velicer, 1997; Weinstein et al., 1998; Ziedonis




& Trudeau, 1997). Different kinds of interventions are thought to bemost beneficial at different SOCs, just as successful self-change isthought to depend on “doing the right things (processes) at the righttime (stages)” (Prochaska, DiClemente, & Norcross, 1992, p. 1110).The stage model is now being used in HIV-prevention programs spon-sored by the Centers for Disease Control and Prevention (Prochaska,DiClemente, & Norcross, 1994). Self-help manuals (Prochaska,DiClemente et al., 1994) and computerized expert systems (Veliceret al., 1993) provide stage-matched advice. These approaches appealto the twin goals of managed care: more effective treatment and costcontainment.

Early evaluation results provide less than unanimous support forthese methods. An expert system intervention, based on thetranstheoretical model, had no effects on smoking prevention or ces-sation in one randomized trial (Aveyard et al., 1999). In the ProjectMATCH study, participants with high initial motivation did equallywell in each of three different treatments. At 4 months, cognitivebehavioral treatment seemed superior to motivational enhancementtherapy for outpatients with low motivation, but this pattern reversedover time, with motivational enhancement becoming superior to cog-nitive behavioral treatment in this subgroup. This finding was onlysignificant at one point in time (15 months), for one of two outcomesexamined, and only among outpatients (Project MATCH ResearchGroup, 1997). The research group concluded that

except for psychiatric severity, there is not convincing evidence ofmajor treatment matching effects. Observed effects are sufficientlysmall and circumscribed that . . . we can conclude that they are clini-cally insignificant when making triaging decisions. . . . These resultssupport wider latitude in the triaging process with less need to matchbasic client characteristics to any of these . . . treatments, if they areimplemented carefully as individual therapy by well-trained therapists.(Project MATCH Research Group, 1997, p. 23)

The stage model has also been used to justify the rationing and allo-cation of treatment resources. For instance, future intentions to quithave been used as a criterion for rationing smoking cessation services(Zhu et al., 1996, as cited in Pierce et al., 1998). And the stage modelhas been viewed as providing useful criteria for allocating parent edu-




cation and family preservation services and determining whether chil-dren should live with parents who have been abusive or neglectful(Gelles, 1996).

The utility of stage-based interventions “depends on one’s ability toidentify stages accurately and efficiently” (Weinstein et al., 1998,p. 298). If there are not discrete stages, how can stage-based interven-tions or rationing/allocation schemes succeed? Rationing or alloca-tion of services by SOC may deprive some people of potentially bene-ficial assistance. Matching participants to treatments by stage may notbe productive and may detract from more important considerations.Furthermore, motivational approaches, thought to be useful in theearly stages, “may have more widespread applicability than previ-ously thought” (Project MATCH Research Group, 1997, p. 24), atleast in the treatment of alcoholism.

SUMMARY AND DISCUSSION

THE SOCS

Since the early 1980s, there have been several revisions of theSOCs model. At present, a five-stage model is most popular. Yet thesestages are defined and measured in a variety of ways. Stage algorithmsare based on several criteria—including past behavior and somewhatarbitrary time frames—which are not consistent across studies. Con-cerns about item construction and the validity of SOC scales have alsobeen noted (e.g., K. B. Carey et al., 1999; Davidson, 1998). The algo-rithms and scales tap different constructs, and there is little evidenceof concordance between them. Without consensus on the constructsand criteria used to define stages, it is not clear what the stages reallyrepresent.

Studies that use independent measures of each stage find that rela-tively few people are involved in one stage at a time. With the possibleexception of precontemplation, the stages do not appear as discretestates, and there are motivational differences among those classified inprecontemplation (Norman et al., 1998; Stotts, DiClemente,Carbonari, & Mullen, 1996). Claims that the stages are discrete, quali-




tatively different states point to associations with other variables fromthe transtheoretical model; that is, individuals in the early stages arethought to differ from those in later stages in use of the processes ofchange, decision balance, and sense of self-efficacy (DiClemente &Prochaska, 1998). However, findings that do not support hypothesizedrelationships between these variables have been reported (Beldinget al., 1995; Davidson, 1998; Pollak et al., 1998). The lack of consis-tent evidence for distinct stages may be due to flaws in stage measuresor in the conceptual model of SOCs. Here, we focus on several unre-solved conceptual issues.

From 1983 to the present, some researchers have viewed the SOCsas artifacts of measurement models.8 If “individuals are classified into‘stages’ of change depending on their assumed value on the continu-ous latent ‘readiness’ [for] change variable” (Hedeker et al., 1999,p. 62), then stage categories do not share the same form as the constructthey are supposed to represent. In Kerlinger’s (1986) words, this measurement model is not isomorphic to reality (p. 396). Because “readi-ness” can be measured continuously (Martin et al., 1996), stage classi-fication results in a substantial loss of information. The advantages ofcontinuous measures have been noted by developers of the model(Velicer et al., 1999), who favor continuous measures of the decisionbalance, processes of change, self-efficacy, and other variables—butnot readiness for change. Prochaska and DiClemente (1998) arguedthat the stage model is a framework for organizing other continuousvariables and processes. The idea that readiness for change may alsobe a continuous variable has not been fully examined.

There is some confusion about how the stages relate to readiness forchange. Some authors equate precontemplation with low motivationand maintenance with high motivation (Ziedonis & Trudeau, 1997).However, it seems that motivation or readiness for change would behighest in the middle stages of preparation and action; people who aremaintaining recent changes are not necessarily ready to change again.Hence, there may be a curvilinear relationship between stage assess-ments and readiness for change. (If this is true, questions can be raisedabout the current practice of computing overall readiness scores bysubtracting Precontemplation scores from the sum of scores on theother three URICA scales, including Maintenance.) This might




explain apparent curvilinear relationships between the stages andother variables.

The SOCs represent more than readiness. The stages are defined inrelation to several disparate constructs regarding intentions andbehavior (Bandura, 1997, 1998; Davidson, 1998). Because there is lit-tle evidence that certain combinations of cognitions and behaviorsproduce (or are equivalent to) discrete states, this amalgam of vari-ables—such as perceptions of problem locus and severity, past changeefforts, frequency of current behavior, and future intentions—may notbe valid. The stage model may be too reductionistic and might obscurebetter understanding of underlying constructs, relationships betweenthem, and their relative importance in change efforts.

CHANGE OVER TIME

The stage model posits an orderly, often cyclical progression thathas not been demonstrated empirically. As Sutton (1996) observed,“Motivation or intention to change may be more realistically thoughtof as a continuum with no necessary assumption that people movealong this continuum in one direction or through a sequence of dis-crete stages” (p. 203). Similarly, problem perceptions, intentions, andbehavioral adaptations (e.g., behavioral frequency) may fluctuateindependently, in various ways, and in no particular order. InBandura’s (1997) view,

people do not recycle through stages. They fluctuate in their struggle toexercise control over their health behavior. . . . In these behavioral fluc-tuations, which can occur quickly in some health domains, people arevarying in their self-regulatory command, not undergoing repeatedtransformational changes. (pp. 413-414)

The order of the stages indicates that cognitive change precedes (orshould precede) behavioral change. There is some evidence that thisbasic progression is not universal. In fact, behavioral change precedessome cognitive changes (Velicer et al., 1996) and can occur withoutcontemplation. McConnaughy et al. (1983) identified two small clus-ters called noncontemplative action and nonreflective action, charac-terized by average or high Precontemplation and Action scores and




low Contemplation scores. Participants in the nonreflective actioncluster “could be characterized as taking action while not acknowl-edging that a problem exists” (McConnaughy et al., 1983, p. 373).Other illogical groups have been found. Whether logical or not, some-times people change in response to life events (e.g., the onset or diag-nosis of serious health problems, births, deaths, divorce) or coercionwithout much reflection or prior intent to change. For example, Stottset al. (1996) found that the decision to stop smoking can be madequickly and without resolving ambivalence about smoking, that is,without passing through contemplation and preparation. Progressionthrough the SOCs does not seem inevitable (Davidson, 1998).

MISINTERPRETATIONS AND OMISSIONS

Before commenting on the strengths of the stage model and direc-tions for further research, we take note of some disconcerting trends inthe literature on the SOCs. First, knowledge about the stage model isoften misrepresented. There have been many claims that the stagemodel has sound construct and predictive validity (e.g., Crittendenet al., 1998; Morera et al., 1998; O’Hare, 1996b), despite mountingevidence to the contrary. Proponents claim that findings in support ofthe model have been replicated repeatedly (Martin et al., 1996;Prochaska, DiClemente, Velicer et al., 1992), but this appears trueonly with respect to the identification of stages using algorithms thatforce participants into mutually exclusive groups (a tautology) andwith regard to relationships between algorithm stage and other con-structs (e.g., the decision balance and processes of change). Authorsstate that “staging addictive behaviors has been accomplished rathersuccessfully” (DiClemente & Prochaska, 1998, p. 8) and cite as evi-dence studies that do not actually support the stage model (Beldinget al., 1996; DiClemente & Hughes, 1990; Isenhart, 1994; Tsoh, 1995).“Many of the data taken as supportive of the stage model are arguablymore consistent with a continuous model” (Davidson, 1998, p. 36).

Also troubling is the exclusion of illogical data from analysis inseveral studies. By eliminating participants whose profiles on SOCscales are not clear (McConnaughy et al., 1983), do not fit the logicalstage categories (Heather et al., 1993), or do not show stability or for-




ward progression over time (Prochaska et al., 1991), researchers havemade rather selective use of their data. Close examination of theseexcluded cases could lead to better understanding of the stage model’slimitations and generalizability, development of alternative explana-tions, and more robust models of stability and change.

A few studies present findings in support of the model in sufficientdetail but only mention results (e.g., of principal components analy-sis) that contradict the model (Costa, 1990; Lerner, 1990;Rosenbloom, 1991). As in other areas, it may be that research findingsthat support a hypothesis are more likely to be published than thosethat do not.

Finally, the language used to describe the stage model oftenobscures its underlying constructs. Central concepts are sometimesdescribed in contradictory terms; for example, we read about “stablepatterns of change” (Norman et al., 1998, p. 142) and “the stage con-tinuum” (Pallonen et al., 1998, p. 305). Other terms (such as externalvalidity) are used in ways that are unique and confusing. Overall, thereis a need for greater care and rigor in the research and literature onSOCs.

THE APPEAL OF SOCS

From the beginning, the transtheoretical model has had its feet intwo worlds: in the rarified realm of health science and psychotherapyand in the self-changing experiences of many. It is positioned as both anew scientific revolution and an “everyman” theory, derived from les-sons learned from “thousands of ordinary people” (Prochaska &Velicer, 1997, p. 11). As Davidson (1992) noted, the stage model fitswith recent interest in the consolidation of common themes acrossproblem behaviors, therapeutic systems, and disciplines, yet it is easyto understand and seems to reflect what we think we already know.The model “seems to have caught the current mood and makes someof us feel more optimistic” (Davidson, 1992, p. 822).

The stage model appeals to certain modern intellectual conceptual-izations of human behavior. Based on the rational actor model, itassumes that change is often the result of careful (cognitive) consider-ation of alternatives and their consequences. If our thoughts and




behavior are connected in a rather logical, orderly fashion and if weunderstand these connections, then we might be better able to explainand predict behavior. These are important social science goals. InSutton’s (1996) view, the SOCs

should be thought of not as a descriptive model but as a prescriptivemodel—a model of ideal change. It prescribes how, from the viewpointof a therapist or health educator, people should change and suggestshow they might be encouraged or helped to change. (p. 204)

If it is best to think about a problem and prepare for change before act-ing (presumably because this will produce more substantial, longerlasting gains), then the change process might be facilitated by motiva-tional and cognitive-behavioral therapies and guidelines for self-help.

Most commentators agree that the model has heuristic value(Davidson, 1992; Pierce et al., 1998; Stockwell, 1996; Sutton, 1996).By suggesting that addictions and other behavioral problems are noteasily remedied, the model may encourage greater patience and per-sistence in change efforts. It also underscores the importance of mea-suring progress toward realistic objectives (DiClemente & Prochaska,1998).

It appears that the widespread acceptance of this model was notbased on careful consideration of the evidence for it. Davidson (1992)suggested that, like people who make “apparently illogical but comfort-able decisions” about substance abuse and other problem behaviors, theSOCs have become widely accepted because we “grasp at the heuristicsand partial truths which make us feel most comfortable” (p. 822).

CONCLUSIONS AND FUTURE DIRECTIONS

The assumption that there are common SOCs across a range of situ-ations, problem behaviors, and populations is not borne out by empiri-cal data. Nor is there consistent or convincing evidence of discreteSOCs in relation to specific problem behaviors, such as substanceabuse (K. B. Carey et al., 1999) or cigarette smoking. To our knowl-edge, there are no published studies of progression through the entirestage sequence.




Like most stage models, this one offers some new and potentiallyuseful ways of thinking about how people grow and change. But aswith other stage models, its descriptions of people and processes arenot particularly accurate or generalizable. Successful change pro-cesses may vary depending on the nature and complexity of the targetbehavior, presence of other problems, external stressors and supports,and cultural context. The search for a generic, underlying structure ofbehavioral change has led to unnecessary reductionism, reliance on aset of categories that do not reflect qualitatively different states, andadherence to assumptions about stage progression that have not beensupported. The model cannot have much practical utility for thedesign or allocation of treatment services if its basic tenets do not holdup. It is time to seriously consider alternative conceptualizations ofchange processes.

Although a stage model may have greater intuitive appeal, a contin-uous model of readiness for change is more parsimonious and may bemore easily integrated with related concepts from other theories(Budd & Rollnick, 1996). As indicated above, a continuous modelmay fit the data better than a stage model, although continuous mea-sures of readiness for change have not yet been thoroughly tested. It isalso not clear what the continuum really represents and whether thereis one continuum (e.g., of readiness for change) or several. Budd andRollnick (1996) emphasized degrees of intention, whereas Stockwell(1992) would prefer to focus on a continuum of preparedness. Consid-erable work will be needed to develop clear definitions and betterunderstanding of motivation, intentions, readiness for change, andpreparation for change.

As K. B. Carey and her colleagues (1999) noted, it is important todistinguish readiness for change from readiness to participate in a par-ticular treatment; some stage assessments appear to tap both con-structs. Previously viewed as a client attribute, motivation for treat-ment is better understood as a result of the interactions of certainclient, therapist, and environmental characteristics (Miller, 1985).Indeed, some barriers to treatment participation are not related to cli-ent motivation or readiness for change (Kazdin, Holland, Crowley, &Breton, 1997). Ryan, Plant, and O’Malley (1995) showed that intrin-




sic and extrinsic motivation for alcoholism treatment have differentrelationships to treatment participation and outcome.

In the transtheoretical model, readiness for change is viewed as aclient attribute, albeit one that can be influenced. This view may beincomplete. Although cognitive appraisals of the pros and cons of theproblem behavior and self-efficacy have been examined in relation tothe SOCs, the model pays little attention to affective states. Feelingsconnected with behavioral problems and with the prospect and pro-cesses of change (depression, anxiety, fear, etc.) are likely to influencereadiness for change and the change process. Ripple, Alexander, andPolemis (1964) described motivation as a balance between discomfortand hope. For some people, this may be more important than the deci-sion balance.

Rather than a progression through stages, change can come aboutswiftly, often as a result of life events or external pressures. Thechange process is likely to vary, depending on whether motivation forchange is internal or external (Stotts et al., 1996) and whether a phar-macological dependence is present (M. P. Carey, Kalra, Carey,Halperin, & Richards, 1993). In the future, emphasis should be placedon understanding variations in patterns and processes of change thatare associated with problem types, social settings, and cultural con-texts. Readiness for change and change processes may mean very dif-ferent things to different people.

Future research should examine a variety of potential influences oncognitive and behavioral change. For instance, problem severity, habitstrength, and frequency of a problem behavior can affect change pro-cesses (Velasquez et al., 1999). The client’s views of nature, causes,and course of the problem (Leventhal, Lambert, Diefenbach, &Leventhal, 1997) and ability to manage stress and anxiety (M. P. Careyet al., 1993) may be important as well. Therapist style and environ-mental characteristics (Miller & Tonigan, 1996)—such as externalstressors, competing demands, and practical constraints—can alsoaffect motivation and change. Whenever possible, continuous mea-sures should be used, and variables ought to be treated as orthogonaldimensions (kept separate) to examine relationships between them.As Prochaska and DiClemente (1998) noted, some continuous vari-ables do not shift in a linear fashion over time, but change in a




curvilinear and quadratic manner. Stages are not the only way to cap-ture nonlinear processes and relationships.

We agree that the transtheoretical model offers a rich heuristic per-spective on change (Prochaska & DiClemente, 1998), but given thelack of consistent evidence for the SOCs, great care must be taken inapplications of the stage model. Stage-matched interventions seempremature and ill advised. A more realistic approach is taken by Millerand Tonigan (1996), who provided clients with feedback on theirSOCRATES scores “as a starting point for discussion about theirmotivation for change” (p. 88).

Indeed, SOCs “have provided the framework for integrating a num-ber of more continuous variables from social learning, decision mak-ing, and health attitudes areas into a larger, more dynamic perspectivethat can complement . . . other models” (Prochaska & DiClemente,1998, p. 40). The stage model’s contributions—emphasis on cognitiveprecursors and correlates of behavioral change, the concept of readi-ness, and nonlinear movement—can be retained without a stagetheory.




256 APPENDIXStudies Reviewed by Topic

Study Sample Stage Measure Stages Examined

Smoking cessationClements-Thompson, Haddock, 10,136 smokers required to quit PC = not planning to “stay quit” after training, PC, C, A

Lando, and Talcott (1998) during basic military training C = thinking about staying quit, A =to stay quit

Crittenden, Manfredi, Lacey, 495 female smokers at public Algorithm 3 PC-1, PC-2, PC-3, C, PWarnecke, and Parsons (1994) health clinics

Crittenden, Manfredi, Warnecke, 1,275 female smokers, 686 with Algorithm 3 PC-1, PC-2, PC-3, C, PCho, and Parsons (1998) follow-up data

DiClemente and Prochaska 63 smokers who quit on their own Retrospective reports on three time periods: (a) Decision to change,(1982) or were participating in one of decision to quit to first efforts to quit, (b) first active change,

two group programs efforts to 2 weeks after last cigarette, (c) 2 maintenanceweeks after last cigarette to present

DiClemente et al. (1991) 1,466 smokers Algorithm 2 PC, C, PDijkstra, Bakker, and DeVries 184 Algorithm 2 (without past quit attempt criterion) PC, C, P

(1997)Dijkstra, DeVries, and Bakker 275 Algorithm 2 (without past quit attempt criterion) PC, C, P, A, M

(1996)Farkas, Pierce, Gilpin, et al. 2,066 adult smokers (stratified Algorithm 2 PC, C, P

(1996) random sample)Farkas, Pierce, Zhu, et al. (1996) 2,066 smokers (as above) Algorithm 2 PC, C, PFava, Velicer, and Prochaska 4,144 smokers Algorithm 2 PC, C, P

(1995)Herzog, Abrams, Emmons,Linnan, and Shadel (1999) 1,390 smokers Algorithm 2 PC, C, P, A, M

606 at 1-year follow-up413 at 2-year follow-up

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Martin, Velicer, and Fava (1996) 545 smokers and former smokers Algorithm 1 (with C = seriously thinking about PC, C, A, Mquitting in next 6 months)

Morera et al. (1998) 261 female smokers (general Algorithm 3 (modified) PC-1, PC-2, PC-3, C,community sample) P, A

Norman, Velicer, Fava, and 2,088 smokers in a representative Algorithm 2 PC, C, P; 446 differentProchaska (1998) sample monothetic change

profiles identified;reduced to 37 profilesfor interpretation

Pierce, Farkas, and Gilpin (1998) 2,514 smokers (stratified random Unique algorithm based on frequency of behavior, PC, C, Early P, Interme-sample) intentions, and time frames diate P, Advanced P,

Action, Early M,Advanced M

Pollak, Carbonari, DiClemente, 435 adult smokers (secondary Algorithm 2 C, P, ANieman, and Mullen (1998) analysis of subsample from

a larger study of volunteersfor a smoking-cessation study,N = 730)

Prochaska and DiClemente 872 participants changing their Algorithm 1 PC, C, A, M, Relapse(1983) smoking habits on their own

Prochaska, DiClemente, Velicer, 866 smokers Algorithm 1 PC, C, A, M, RelapseGilpin, and Norcross (1985)

Prochaska, Velicer, Guadagnoli, 960 smokers and former smokers Algorithm 1 PC, C, A, Mand Rossi (1991)

Snow, Prochaska, and Rossi Smoking cessation among 191 Three-item staging algorithm PC, C, P(1992) recovering problem drinkers

Solomon, Secker-Walker, Skelly, 521 pregnant smokers Algorithm: PC = not intending to quit, PC, C, P, Aand Flynn (1996) C = has not cut down but intends to quit,

P = has cut down and intends to quit, A = quit

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APPENDIX Continued


Stotts, DiClemente, Carbonari, Sample 1: 89 pregnant smokers Algorithm 2 P, Aand Mullen (1996) Sample 2: 120 nonpregnant

smokersVelicer, Hughes, Fava, Prochaska, 644 smokers Algorithm 2 PC, C, P, A; cluster

and DiClemente (1995) analysis of subtypeswithin stage

Velicer, Norman, Fava, and 2,967 smokers in representative Algorithm 2 PC, C, PProchaska (1999) sample

Velicer, Rossi, Prochaska, and 698 smokers Algorithm 2 PC, C, A, MDiClemente (1996)

Wilcox, Prochaska, Velicer, and 703 smokers, self-change efforts Algorithm 1 PC, C, A, M, RelapseDiClemente (1985)

Tobacco use acquisitionElder et al. (1990) 358 high school students Stages of Tobacco Aquisition—Revised PC, DM, MPallonen, Prochaska, Velicer, 744 high school students Algorithm 4 Acquisition stages: aPC,

Prokhorov, and Smith (1998) aC, aP; cessationstages: RA (recentacquisition), PC, C,P, A, M

Stern, Prochaska, Velicer, 202 high school students Stages of Tobacco Acquisition Three components: PC,and Elder (1987) DM, M; nine clusters

(PC, C, DM, A, M;four were notinterpretable)

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Substance abuseAbellanas and McLellan (1993) 41 methadone-maintained, opioid- URICA (32-item) PC, C, A, M

dependent male veterans withconcurrent cigarette and cocaine,dependence problems

Belding (1993); Belding, Iguchi, 276 methadone maintenance patients Algorithm 2, URICA (34 items) PC, C, P, A, Mand Lamb (1996) (algorithm); PC, C,

A, M (scales) (Mstage not validated)

Belding, Iguchi, and Lamb 81 methadone maintenance patients Algorithm 2, URICA (16 items) PC, C, P, A (algorithm);(1997) PC, C, A, M (scales)

Belding, Iguchi, Lamb, Lakin, 276 methadone maintenance patients Algorithm 2 PC, C, P, A, Mand Terry (1995)

Budd and Rollnick (1996) 174 male heavy drinkers Readiness to Change Questionnaire (12 items) PC, C, A factors (thethree-factor structurewas not confirmed);overall readinessscore

Cady, Winters, Jordan, Solberg, 234 adolescents in in/outpatient Problem Recognition Questionnaire (PRQ) forced 3-factor solutionand Stinchfield (1996) substance abuse treatment yielded: C, C/P, P

Carney and Kivlahan (1995) 404 alcohol and drug users seeking URICA (28 items) PC, C, P, A; fourtreatment clusters: PC,C,

Ambivalent, and PDiClemente and Hughes (1990) 224 adult outpatients seeking URICA (28 items) PC, C, P, Ambivalent,

alcoholism treatment and Uninvolvedclusters

Farabee, Nelson, and Spence 176 outpatient substance abusers Texas Christian University Self-Rating Form C, Determination, A(1993)

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APPENDIX Continued


Heather, Rollnick, and Bell 174 hospitalized males with low Readiness to Change Questionnaire (12 items) PC, C, A(1993) levels of alcohol dependence

Hutchison (1996) 95 inpatient/outpatient substance URICA (32 items) PC, C, P, A, Mabusers

Isenhart (1994) 165 inpatient substance abusers SOCRATES (20 items) C, Determination, A(male veterans) factors; Ambivalent,

Uninvolved, Activeclusters

Isenhart (1997) 125 males receiving inpatient SOCRATES(20 items) C, D, Atreatment for alcohol dependence

Jefferson (1991) 110 drug or alcohol addicts in URICA (32 items) PC, C, A, Moutpatient treatment

Kavanagh, Sitharthan, and 121 volunteers in study of Readiness to Change Questionnaire (12 items) PC, C, A, MSayer (1996) correspondence treatment

for alcohol abuseLamb, Belding, and Festinger 185 cocaine users in a research URICA based PC, C/A, M

(1995) clinic studyMiller and Tonigan (1996) 1,672 adults with alcohol problems SOCRATES, short form (20 items) Taking steps, recogni-

receiving outpatient treatment tion, ambivalenceor aftercare following residentialor day treatment

Project MATCH Research Two independent randomized Subset of URICA Overall readinessGroup (1997) clinical trials; 952 alcohol depen- score (C)

dent adults receiving outpatienttherapy, 774 adults receivingaftercare therapy followinginpatient or day hospital treatment

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Rollnick, Heather, Gold, 141 excessive drinkers identified in 20 URICA items modified to create the 12-item PC, C, Aand Hall (1992) medical settings Readiness to Change questionnaire

Ryan, Plant, and O’Malley 98 adults seeking outpatient Treatment Motivation Questionnaire (26 items) Four factors: internal-(1995) treatment at an alcoholism clinic ized motivation,

external motivation,help seeking, con-fidence in treatment

Tsoh (1995) 710 former addicts in two 32 URICA items modified to create the 16-item PC, C, A, M; sixresidential programs Change Assessment for Drug Use clusters

Velasquez, Carbonari, and 132 alcohol-dependent patients in URICA-A (28 items) and a single Readiness to P, C, A, M and overallDiClemente (1999) a public mental health clinic’s Change scale score readiness score

outpatient dual diagnosis programWilloughby and Edens (1996) 144 inpatient substance abusers URICA (32 items) PC, C, A, M; two

(male veterans) clusters (PC, C/A)Ziedonis and Trudeau (1997) 497 adults with schizophrenia or Five-question stage-of-change algorithm adapted PC, C, P, A, M

schizoaffective disorder in an to assess motivation for quitting each specificoutpatient mental health clinic; substance, based on current practice and future244 were dually diagnosed with intentat least one substance use disorder

HIV risk reductionRhodes and Malotte (1996) 560 injection drug and crack Unique algorithm based on frequency of drug use PC, C, P, A, M

cocaine users recruited on streets and safe sex, strength ofintentions, and time frames

Stevens and Estrada (1996) 343 IV-drug and crack cocaine users Forced choice among items, one item per stage PC, C, A, Mand 44 female sexual partners

Mental healthCosta (1990) 170 outpatients URICA (32 items) PC, C, A, MFranko (1997) 16 women in brief treatment for URICA (32 items) PC/C, A/M

bulimia

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APPENDIX Continued


Hilburger (1995) 193 adults with severe and URICA items modified to create the 32-item PC, C, A, M; fourpersistent mental illness Change Assessment Questionnaire–Severe clusters (PC, C/P,

and Persistent Mental Illness A, M)McConnaughy, Prochaska, 155 adult outpatients Initial pool of 125 items used to develop the PC, C, DM, A, M (DM

and Velicer (1983) 32-item URICA stage was notvalidated);18 clusters (9 wereuninterpretable)

McConnaughy (1984); 327 adult outpatients URICA (32 items) PC, C, DM, A, M (DMMcConnaughy, DiClemente, stage was notProchaska, and Velicer (1989) validated); eight

clustersO’Hare (1996a) 376 outpatients URICA (32 items) PC, C, A, MO’Hare (1996b) 376 outpatients URICA (32 items) PC, C, A, M; five

clustersSatterfield, Buelow, Lyddon, 88 outpatients URICA (32 items) PC, C, A, M

and Johnson (1995)Smith, Subich, and Kalodner 74 clients in counseling URICA (32 items) PC, C, A, M

(1995)Clinical drug trials

Beitman et al. (1994) 126 outpatients with panic URICA (32 items) PC, C, A, M; threedisorder and agoraphobia clusters (high PC,

low PC, belowaverage on all)

Reid, Nair, Mistry, and Beitman 113 outpatients with panic disorder URICA (32 items) PC, C, A, M(1996) and agoraphobia

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Wilson, Bell-Dolan, and 131 outpatients with generalized URICA (32 items) P, C, A, MBeitman (1997) anxiety disorder

OtherCardinal (1997) 235 volunteers in a study of Algorithm 2 (with P = presently exercising but not PC, C, P, A, M

exercise acquisition regularly)Hedeker and Mermelstein (1998) 3,146 adolescents in study of Algorithm based on current practice (sunscreen PC, C, A

sunscreen use use) and future intentHedeker, Mermelstein, and 3,185 adolescents in study of Algorithm based on current practice (sunscreen PC, C, A

Weeks (1999) sunscreen use use) and future intentNigg et al. (1999) 19,266 older adults from a Algorithm 2 PC, C, P, A, M

representative sample of healthmaintenance organizationmembers

Investigated “stage distribution”on 10 behaviors: seatbelt use,avoidance of high-fat food,eating a high-fiber diet,attempting to lose weight,exercising regularly, avoidingsun exposure, sunscreen use,attempting to reduce stress,stopping smoking, andconducting cancer self-exams

Prochaska, Norcross, Fowler, 184 hospital staff members in a URICA (32 items) PC, C, A, MFollick, and Abrams (1992) weight control program

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APPENDIX Continued


Prochaska, Velicer, et al. (1994) Of participants, 764 in smoking Most studies used Algorithm 2; URICA (32 items) PC, C, P, A, M (somepresent results from 12 separate cessation, 156 quitting cocaine, was used by Lerner (1990) and Rosenbloom studies did notstudies, including Lerner 123 weight control, 180 high-fat (1991); exercise study used a 10-step stage include P)(1990) and Rosenbloom (1991) diets, 159 adolescent delinquency, ladder; Lerner also used a unique algorithm

213 safer sex, 345 condom use, with forced choice among the following: PC =227 sunscreen use, 698 radon “I don’t need to change to stay out of trouble”;gas exposure, 717 exercise C = “I think that I will need to change to stayacquisition, 141 mammography out of trouble, but haven’t done anything yet”;screening, 135 physicians’ A = “I know I need to change to stay out ofpreventive practices with smokers trouble and I am doing something about it right

now”; M = “I have already made the changesI need to stay out of trouble, I just need tokeep it up.”

Suris, del Carmen Trapp, 81 Mexican American women in URICA (16 items) Five clusters or “profiles”DiClemente, and Cousins weight loss study (PC, Discouraged,(1998) Ambivalent, C,

Participation)Trigwell, Grant, and House 361 outpatients with diabetes SOCRATES (40 items) PC, C, Determination,

(1997) mellitus A, MVan Duyn et al. (1998) 2,811 adult respondents to random Algorithm based on current practices, future PC, C, P, A, M

digit dial telephone survey intentions, and efficacy regarding fruit andvegetable consumption

NOTE: URICA = University of Rhode Island Change Assessment; URICA-A = University of Rhode Island Change Assessment–Alcohol; SOCRATES =Stages of Change Readiness and Treatment Eagerness Scale. PC = precontemplation, C = contemplation, P = preparation, DM = decision making, A = action,M = maintenance. Algorithm 1: PC = no intention to change within 1 year; C = seriously thinking about changing within 1 year; A = changed within the last 6months; M = changed more than 6 months ago. Relapse = failed attempt to change within the last year (Prochaska & DiClemente, 1983). Algorithm 2: PC = not

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thinking about changing within 6 months; C = seriously thinking about changing in next 6 months; P = failed change attempt in the last year and seriouslythinking about changing in next 30 days; A = change made within past 6 months; M = change made over 6 months ago (Prochaska, Velicer, et al., 1994). Algo-rithm 3 (in relation to smoking): PC-1 = not seriously thinking of quitting, not planning to quit, and not thinking of cutting down; PC-2 = not seriously thinkingof quitting and not planning to quit but seriously thinking of cutting down; PC-3 = seriously thinking of quitting or planning to quit but not within thenext 6 months; C = seriously thinking of quitting and planning to quit within 6 months but either not planning to quit within 1 month or made no intentional24-hour quit attempt within the past year; P = seriously thinking of quitting and planning to quit within 1 month and intentionally quit for at least 24 hourswithin the past year (Crittenden et al., 1994, p. 500). Algorithm 4 (smoking acquisition and cessation): aPC = acquisition precontemplation (participants whosay they do not smoke regularly and will not try smoking in the next 6 months); aC = acquisition contemplation (participants who might try smoking within6 months); aP = acquisition preparation (might try smoking in the next 30 days); RA = recent acquisition (smoked regularly for less than 6 months); PC =precontempation (smoking for more than 6 months and not planning to quit within the next 6 months); C = contemplation (seriously considering quittingwithin 6 months); P = preparation (planning to quit in the next 30 days); A = action (quit less than 6 months ago); M = maintenance (quit more than 6 monthsago). Note that these categories are not exhaustive. Participants who ever smoked chose between three items: “I have tried smoking a few times,” “I used tosmoke regularly but I quit,” and “I am a smoker.” These options do not cover teens who tried smoking more than a few times but do not consider themselves tobe (or have ever been) regular smokers. Forced choices between “less than 6 months ago” and “more than 6 months ago” do not cover people who started,stopped, or tried to quit smoking 6 months ago.

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NOTES

1. Drawn from various systems of psychotherapy, these basic processes of change are as fol-lows: consciousness raising, self-liberation, social liberation, self-reevaluation, environmentalreevaluation, counterconditioning, stimulus control, reinforcement (or contingency) manage-ment, dramatic relief, and helping relationships (Prochaska & DiClemente, 1984). Certain pro-cesses are thought to be emphasized more than others in particular stages.

2. These criteria are less stringent than those proposed by Bandura (1997) but more accu-rately reflect specific assumptions of the stages of change model, in which progress is consideredreversible (Prochaska, 1995).

3. Related instruments include the Stages of Change Readiness and Treatment EagernessScale (Miller & Tonigan, 1996) and the Readiness to Change Questionnaire (Rollnick, Heather,Gold, & Hall, 1992).

4. For example, in a study by McConnaghy, DiClemente, Prochaska, and Velicer (1989),precontemplation, contemplation, and action components each contained one item with a load-ing less than .4 and one action item loaded (at .63) on the contemplation component. Hilburger(1995) found that two action items and one contemplation item loaded (at .4 or higher) on themaintenance component, one contemplation item loaded on the action component, and onlythree of eight contemplation items loaded on the contemplation component.

5. In Miller and Tonigan’s (1996) analysis, Ambivalence was unrelated to the other two fac-tors; Recognition and Taking Steps were positively correlated (r = .33).

6. For Contemplation and Maintenance, Pearson’s r= .45; for Contemplation and Action, r=.50; for Action and Maintenance, r = .48 (McConnaughy et al, 1989, p. 497).

7. This does not include 196 participants who regressed from contemplation, because allmembers of this group were classified in precontemplation at the end.

8. “In the reality of therapy, these stages are not assumed to be discrete nor is movement succes-sive. However, it is [our] intent . . . to depict the stages as distinct and consecutive as a useful frame-work for the purpose of measurement” (McConnaughy, Prochaska, & Velicer, 1983, p. 369).

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Julia H. Littell is an associate professor at the Graduate School of Social Work andSocial Research at BrynMawrCollege.Her scholarly interests include childwelfare pol-icy and practice, child maltreatment, and the processes and outcomes of family- andcommunity-based social services. She has published articles on family preservation andfamily support programs and is coauthor of Putting Families First: An Experiment inFamily Preservation (Aldine de Gruyter, 1994). Contact the author at [email protected].




Heather Girvin is a doctoral candidate and instructor at the Graduate School of SocialWork and Social Research at Bryn Mawr College. Her scholarly interests include childmaltreatment; poverty and depression, particularly among caretakers in the child abuseand neglect system; and systemic problems and innovations in child welfare and juvenilejustice. Contact the author at [email protected].




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