Journal of Personality and Social Psychology1996. Vol. 71. No. 6. 1084-109!

Copyright 1996 by the American Psychological Association. Inc.0022-3514/96/S3.00

Relationship Between Social and Personal Identities:Segregation or Integration?

Anne Reid and Kay DeauxCity University of New York

Recognition that self-representation includes both social and personal identities raises questionsabout the cognitive organization of these elements. Two models of identity structure are compared:(a) a segregation model (D. Trafimow, H. C. Triandis, & S. G. Goto, 1991), which assumes that(social) identities and (personal) attributes are two distinct categories, and (b) an integration model(K. Deaux, 1992), which proposes that identities and attributes often coexist in a limited set ofcognitive structures. Clustering of self-relevant information in free recall was used to assess cognitiveorganization in a sample of 57 students. Identities and attributes clustered separately at greater-than-chance rates, consistent with the segregation model. More detailed analysis of recall data, in whichindividual patterns of association between identities and attributes were considered, providesstronger support for an integration model of self-representation.

How is the self best characterized? This question has engagedtheorists for nearly a century, dating from the early work ofJames( 1890/1950) and Mead (1934/1962) to the more recentactivity in social cognition (Greenwald & Pratkanis, 1984;Kihlstrom & Cantor, 1984; Linville & Carlston, 1994). Inoffering their analyses of the self, the majority of theorists as-sume some form of multiplicity, believing that the self is moreaccurately and usefully viewed as a composite rather than as aunitary entity. Although theorists agree on this general tenet,diiferent models offer different concepts to parse out the totalself package. Thus, researchers variously view the self as madeup of separate self-schemas (Markus, 1977); actual, ought, andideal selves (Higgins, 1987); possible selves (Markus & Nurius,1986); self-prototypes (Kihlstrom & Cantor, 1984); or rolesand identities (McCall& Simmons, 1978;Stryker, 1980).

One major basis of characterizing aspects of the self, which hasgained increasing prominence in recent years, is the distinctionbetween aspects of identity that are collective and related to socialgroup membership versus features of identity that are personaland presumably more individuating. This general distinction hasseveral related forms. Social identity and self-categorization the-ories (Hogg & Abrams, 1988; Tajfel, 1981; Turner, Hogg, Oakes,Reicher, & Wetherell, 1987) contrast personal identity, in whichthe stress is on personality characteristics and behaviors (e.g., in-

This research was supported by Grant BNS-9110130 from the Na-tional Science Foundation.

We thank Michael Gara, Seymour Rosenberg, and members of theIdentity Research Seminar at the City University of New York GraduateCenter for their helpful comments on an earlier version of this article.Some of the findings reported here were presented at the 101st AnnualConvention of the American Psychological Association in Toronto, On-tario, Canada, in August 1993.

Correspondence concerning this article should be addressed to AnneReid or Kay Deaux, Graduate School and University Center, City Uni-versity of New York, 33 West 42nd Street, New York, New York 10036-8099.

telligent, hardworking) that differentiate one from others, withsocial identities that derive from group memberships (e.g., Afri-can American) and provide the basis for common identification.Luhtanen and Crocker (1992) elaborated on this distinction inproposing two distinct forms of self-esteem, one personal and onecollective, that are based on different aspects of the self-concept.A similar partition between collective and private or individual-istic aspects of the self was made by Greenwald and Pratkanis(1984) and by Triandis (1989).

General agreement that a conceptual distinction can be madebetween group-based and individual-based aspects of the selfis unfortunately not accompanied by a consensual position onvocabulary. Terms for group-based aspects of identification in-clude collective, allocentric, and social identity; the contrastingtype of self-cognition has been termed individualistic, private,idiocentric, and personal identity. Our preference is to use theterm identity, or social identity, to refer to social groups or col-lective categories of membership. We use the term attribute todesignate the personality traits, characteristics, and behaviorsthat an individual uses in self-description. With this terminol-ogy we hope to avoid some of the confusion that results from thediffering theoretical uses of personal identity (Deaux, 1992).Specifically, we want to differentiate our use of identity and at-tribute as elements of self-structure from the theoretical distinc-tion between social and personal identity offered by social iden-tity and self-categorization theories, in which the emphasis ison dynamic shifts in focus of attention (Turner et al., 1987;Turner, Oakes, Haslam, & McGarty, 1994).

The presumption of different aspects of the self, and in par-ticular the distinction between identities and attributes, raisesquestions about the organization of self-structure. With the in-creased interest of investigators in cognitive organization andmemory representation, these questions about self-structuretake on new importance in research on the self.

One Basket or Two?Trafimow, Triandis, and Goto (1991) recently addressed this

question, suggesting two possible models of organization ofself-

1084

SPECIAL ISSUE: SOCIAL AND PERSONAL IDENTITIES 1085

cognitions. One possibility, which they ultimately discarded,was a "one-basket" model in which information about bothidentity and attribute is stored in a single cognitive structure.From this perspective, retrieval of self-related elements frommemory should be predicted solely on the basis of frequency.Thus, if a person has more personal attributes than social iden-tities, then attributes are more likely to be recalled on request.Priming one aspect of the self—for example, social identities—should be no more likely to elicit social identities than attri-butes, according to a one-basket model.

The second model proposed by Trafimow et al. (1991) wastermed a "two-basket" model. This model presumes that per-sonal attributes are stored separately from social identities, thatis, that there are two distinct and nonoverlapping self-represen-tations. This model, which we term the segregation model, isillustrated in the top half of Figure 1.

Self-descriptions within the same basket are assumed to havesome degree of association with one another, by virtue of theircommon category membership. Consequently, if an identity ismade accessible, other identities stored in that same basketshould be more accessible than any personal attributes in theother basket. Trafimow et al. (1991) tested this assumption intwo ways, focusing on the kinds of items listed by respondentson Kuhn and McPartland's (1954) Who Am I? scale. First, theypredicted that priming a social identity would result in morefrequent listing of social identities and that priming a personalattribute would result in more frequent listing of attributes.

The Segregation Model of Identity Structure

The Collective Self The Private Self

ACTIVISTBROTHERFRIENDSTUDENTADVOCATEATHEIST

Advice giverDisciplinedGood listenerNot giftedSkepticalConcernedLogicalFunNot satisfied

The Integration Model of Identity Structure

Cluster #1 Cluster #2 Cluster #3

BROTHERFRIENDGood listenerAdvice giverFun

STUDENTNot giftedLogicalDisciplined

ACTIVISTADVOCATEATHEISTSkepticalConcernedNot satisfied

Figure 1. The relationship between social and personal identities: twoalternative models. Social identities appear in uppercase letters; per-sonal identities or attributes appear in upper and lowercase letters.

Second, they predicted that the retrieval of an item in one cate-gory—for example, a personal attribute—would increase theprobability of retrieving another item in that same category.

In general, their results were consistent with a two-basketmodel. Different primes led to different rates of retrieving eitherattributes or social identities (although the trends were relative,rather than absolute, in that respondents always listed more per-sonal attributes). Furthermore, and more relevant to the pres-ent study, patterns of identity listing showed greater-than-chance clustering by category: When a respondent listed a socialidentity, it was more likely to be preceded by another socialidentity than by an attribute (and similarly for the sequenceof attributes). Although these patterns were in the predicteddirection, the data had a considerably less-than-perfect fit to atwo-basket model, in which all social identities would be re-trieved in sequence and then all personal attributes (or viceversa). This lack of fit to an ideal model suggests that additionalprinciples may be needed to account for the structure of self-representations.

Two Baskets or More?

Trafimow et al.'s (1991) results quite clearly suggest that self-representations are stored in something more complex than asingle cognitive structure. At the same time, they leave openthe question of just what the alternative organization might be.Although the bifurcation of social identity versus personal attri-bute has an appealing simplicity, some investigators have pro-posed that social and personal aspects of the self are not so easilyseparated (Breakwell, 1986; Deaux, 1992, 1993; Rosenberg &Gara, 1985). According to this position, social identities andpersonal attributes are often inextricably linked and thus wouldbe represented within the same cognitive structures. Attributes,defined as personality traits or behaviors, are viewed as not nec-essarily constituting a separate form of identity; rather, they of-ten provide the content and meanings of social categories. Thus,defining oneself as a professor can implicate traits such as intel-ligent, curious, and hard-working. Similarly, the meaning of be-ing a Catholic might be inseparable from seeing oneself as spir-itual, honest, and concerned for the welfare of others. Thismodel, which we term the integration model, is represented inthe bottom half of Figure 1.

From the perspective of the integration model, most individ-uals would have more than two organizing structures for self-related features, many of which would contain some combina-tion of social identity and personal attribute. In the simple ver-sion of this model, a person would have as many structures ashe or she has social identities; each of these social identitieswould, in turn, be associated with a set of personal attributes.In a somewhat more complex version of the integration model,such as that proposed by Rosenberg and Gara (1985), socialidentities that share common attributes might be combined ina single structure, as represented in Figure 1.

The links between identities and attributes are thought tooriginate in at least two ways: (a) from culturally shared socialrepresentations that provide ways of interpreting common so-cial categories (Breakwell, 1993; Moscovici, 1981) and (b)from specific individual experiences that give personal meaningto a categorical self-definition. Although the latter route pro-

1086 REID AND DEAUX

duces more idiosyncratic links between identity and attribute,both routes are consistent with the assumption that attributesprovide meaning to the more general categories of self-definition.

Comparison of the Segregation and Integration Models

Predictions of self-representation, organization, and recallderived from the segregation model are quite straightforward.As outlined by Trafimow et al. (1991), identities referring tosocial or group membership should always cluster together, dis-tinct from the cluster of personal attributes. Making one versusthe other category accessible should increase the probabilitythat a person would draw from the primed category; a shift tothe other category would be predicted only if category accessi-bility changed or if the first category were exhausted.

Predictions from the integration model are somewhat morecomplex and require a more detailed analysis of the social iden-tities that an individual endorses and the specific attributes thatare associated with each social identity. Thus, the integrationmodel would predict that certain attributes are linked with cer-tain identities and that those associated identities should belikely to cluster together in any test of report or recall. Put moreconcretely, the characteristic of intelligence, if associated withbeing a professor, should be more likely to be recalled with thatcategory than with another personal attribute—such as physi-cally fit—that might be primarily linked to the social identity ofmarathon runner. Attributes could be represented in the samecognitive structure if they had similar links to the social identityrepresented in that structure. Similarly, two or more socialidentities might be linked by common meaning: For example,being a psychologist and being a professor, although definablydifferent identities, might have considerable shared meaningand hence occupy a common structure.

We designed this study with three goals in mind. First, weprovide a conceptual replication of Tranmow et al.'s (1991)study in which we assessed identity-attribute sequences using ameasure of recall rather than open-ended listing of identities.Second, we test the power of each model using a different de-pendent measure, specifically the overall clustering of items infree recall. According to the segregation model, recall shouldbe organized in terms of two major clusters. According to theintegration model, recall should be organized in terms of severalclusters, each of which combines identities and attributes.Given the assumption of idiosyncratic identity-attribute links,the appropriate test of the integration model requires an exam-ination of associations between identities on an individual basis.Third, we explore some unique implications of the integrationmodel that predict differences within attribute-attribute andidentity-identity pairings.

Method

Overview

Our goal in this study was to assess the organization of social identi-ties and personal attributes in people's self-representations. In a firstsession, participants named each of their important social identities andprovided a list of trait attributes associated with each. In a second ses-sion, 1 week later, each respondent was asked to recall the identities and

attributes that he or she had named. Clustering patterns in order ofrecall served asan index of cognitive structure. In a third session, severalweeks later, participants rated the degree to which each attribute wasassociated with each identity, providing the data necessary for a hierar-chical classes (HICLAS) analysis of identity structure (DeBoeck & Ro-senberg, 1988).

Participants

Fifty-seven undergraduate students (36 women and 21 men) from alarge New York City college participated in the study. The ages of therespondents ranged from 17 to 25 years with a mean of 19.5. Sixty-onepercent of the sample was White, 9% was African American, 1% wasHispanic/Latino/Chicano, 11% was Asian American, and 7% was WestIndian. The remaining 5% did not specify their ethnicity. Five peoplewere eliminated from the analyses because they failed to provide usabledata. Respondents participated in the study for both course credit(Session 1 and 2)and financial payment (Session 3).

Procedures for Data Collection

Session I. Participants were tested individually in the first session.Each student completed the first stage of the identity elicitation in-terview (Rosenberg & Gara, 1985), which provides a list of identitiesand attributes. To introduce the task, the interviewer explained thatpeople can define or describe themselves in terms of social categories orgroups to which they belong. Some examples of these categories, suchas sex, ethnicity, occupation, and political and religious affiliations, wereprovided for purposes of illustration. After hearing this brief descrip-tion, participants were asked to generate a list of self-relevant socialidentities. The interviewer then asked each respondent to list the attri-butes that he or she associated with each social identity. Finally, respon-dents completed a short demographics questionnaire in which they in-dicated their sex, age, ethnicity, and year in school.

Session 2. One week later, each participant was given a list of theidentities and attributes that he or she had generated in the previoussession, presented now in a single alphabetical order. Alphabetical orderprovided an essentially random ordering of attributes and identities,thus avoiding a bias toward either the integration or the segregationmodel. Participants were asked to rate each item on the list in terms ofits importance to their self-concept, using a 5-point scale on which 1 =not important and 5 = extremely important. We included this step toremind participants of the identities and attributes they had reported aweek earlier, thereby improving rates of recall.

After completing the importance ratings, but before doing the recalltask, students worked for 5 min on a distractor task, which consistedof a word search puzzle on an innocuous topic. The purpose of thisinterpolation was to increase the likelihood that the sequencing of par-ticipants' free recall in the next stage of the procedure would be based onlong-term memory (i.e., self-structure) rather than short-term memory(i.e., the immediately preceding alphabetical order).

When they had completed the distractor task, participants were askedto recall as many of the items from the importance rating task as possi-ble. They were told to list the items in whatever order occurred to them.This recall task provided the data for assessment of identity-attributeorganization.

Session 3. Several weeks later, participants were contacted andasked if they would participate in a third session, for which payment of$ 10 was offered. Twenty-nine members of the original sample agreed toreturn for this additional session.1 Respondents in this sample did not

1 Three participants who did not recall a sufficient number of identi-ties at Session 2 were not contacted for this portion of the study, norwere the 5 people originally eliminated for unusable data in Session 1.

SPECIAL ISSUE: SOCIAL AND PERSONAL IDENTITIES 1087

differ in age, sex, or ethnicity from those students who did not partici-pate in the third session. They also did not differ in identity-relevantmeasures, including the number of identities and attributes generatedat the first session or the number of identities and attributes recalled atSession 2 (all comparisons were nonsignificant).

At this third session, each participant was provided with a set of indi-vidually constructed rating forms on which they were asked to indicatethe extent to which each attribute they had named in the first sessionwas associated with each of their named identities (Rosenberg & Gara,1985). These ratings were made on a 3-point scale (0 = n ever, 1 = some-times, 2 = always); in subsequent data analysis the I and 2 responsesare combined to provide the binary data necessary for HICLAS analysis(DeBoeck& Rosenberg, 1988; Rosenberg* Gara, 1985). This analysis,explained in more detail in the Results section, produces a graphicalrepresentation of a person's identity structure that shows the associationand overlap between both identities and attributes.

Results

Descriptive Summary

Participants in the study listed an average of 7.1 social identi-ties (SD = 1.3), a figure closely approximating those reportedin previous studies of identity (Deaux, 1991; Ethier & Deaux,1994). The average number of attributes generated was 50.4(SD = 23.2). On the recall task in Session 2, participants re-called on average 5.2 (73%) of their social identities (SD = 2.1)and 20.1 (40%) of their attributes (SD = 9.6). In addition, par-ticipants listed an average of 2.1 attributes that were not part oftheir original list. Virtually no identities were falsely recalled.

Analytic Strategy

The basic data for analysis were the respondent's recalledidentities and attributes. Each item in a respondent's list wascoded as either an identity (I), if it referred to a social role or asocial group, or an attribute (A), if it referred to a trait, behav-ior, or other personal characteristic.2

We used three approaches to data analysis, each of which re-lies on the order and sequence in which identities and attributeswere recalled. The first approach is based on the strategyadopted by Trafimow et al. (1991), who used conditional prob-ability scores to assess the likelihood that identities and/or at-tributes succeed each other. In the second approach we consid-ered the overall pattern of clustering in recall lists. To test thesegregation model, two clusters are defined (identities andattributes). For the integration model, which takes into accounteach individual's particular associations between identities andattributes, the number of clusters varies across individuals as afunction of their initial identity listings. The third approach,which uses HICLAS analysis (DeBoeck & Rosenberg, 1988) todetermine associations between identities and attributes, pro-vides a more finely grained analysis of self-structure and allowsthe test of predictions unique to the integration model.

Conditional probabilities of identity-attribute pairings. InTrafimow et al.'s (1991) experiments, participants were giveneither private or collective primes and then asked to completeKuhn and McPartland's (1954) 20-statement "Who am I?"listing, with the assumption that the sequence of self-descrip-tions would reflect self-structure. In the present study, partici-pants were asked to recall material they had generated earlier,

Table 1Mean Conditional Probabilities of Identity and AttributeSequences, With Comparison to Data From Trafimow,Triandis, and Goto (1991, Experiment 2)

Probability Present study Trafimow etal. (1991)

PdIA)

.33

.84

.16

.67

.46

.73

.17

.51

Note. I = identity; A = attribute.

but the assumption that sequence would reflect the organizationof self-representations was the same. In Trafimow et al.'s proce-dure each participant was required to produce 20 statements;in our procedure the length of the recall list could vary (theaverage list length was 25.3 items).

Following the procedure of Trafimow et al. (1991), we calcu-lated four probability scores for each recall list in which one ormore social identities were recalled (N = 51): the probability ofan identity following an identity, p(l\l); the probability of anidentity following an attribute, p(I|A); the probability of anattribute following an attribute, p(A|A); and the probabilityof an attribute following an identity, p( A11) .3 We used within-subject analyses of variance to test the comparisons predictedby the segregation model.

Consistent with the results of Trafimow et al. (1991), theprobability of retrieving an identity given that the previous re-sponse was an identity was greater than if the previous responsewas an attribute, p(I|I) = .33 > p(I|A) = .16, F(l, 50) =18.79,/? < .001. Similarly, the probability of retrieving an attri-bute given that the previous item was an attribute was greaterthan if the previous response was an identity, p(A\ A) = .84 >p(A|I) = .67, F(l , 50) = 18.79, p < .001. These results aresimilar in magnitude and direction to those reported by Trafi-mow et al. (1991, Experiment 2), as shown in Table 1.

It is important to note that these similar patterns of findingsare the result of two somewhat different methods of elicitingidentities. Participants in Trafimow et al.'s (1991) study pro-duced free-response lists after receiving either group or individ-ual primes. In our procedure, participants at the initial session,1 week prior to recall, generated a list of identities and thenproduced a set of attributes for each identity. Each of these pro-cedures might be suspected of biasing the resultant output in adirection favoring the segregation or integration models, respec-tively. The similarity of the patterns, however, argues againstsuch a procedural bias.

2 The designation of an item as an identity or attribute was based onparticipants' initial listings of identities and attributes in Session 1.

3 Trafimow et al. (1991) used a different notation system; they usedG (referring to group) for what we term identities (I) and I (referring toidiocenlric) for what we term attributes (A). In addition, they initiallycoded a category that they termed altocentric, used for characteristicsthat connoted interdependence and sensitivity to others, but the fre-quency of those items was quite low, and the category was not includedin their reported analysis.

1088 REID AND DEAUX

At a conceptual level, the fact that two quite different meth-ods of eliciting self-descriptive material led to similar outcomessuggests that a consistent form of mental representation is guid-ing the output. The precise form of this representation is notclearly specified, however. At a very general level, the assertionthat identities and attributes cluster together separately is sup-ported. Yet these patterns are far from perfect. In the pure case,one would expect that the 111 and A | A figures would both ap-proach 1.0, and the 11A and A11 cases should approximate 0.The obtained data are quite discrepant from these norms, sug-gesting that other strategies of coding self-descriptive informa-tion may also be operating. In the second and third analytic ap-proaches we explored this possibility, providing a basis for con-trasting the segregation and integration models.

Overall patterns of clustering. The analysis of conditionalprobabilities tests the frequency of specific sequential pairs ofidentities and/or attributes. Another approach to analyzinglists of self-relevant material is to consider the overall pattern orclustering of items.

The segregation model would predict that recall would be or-ganized in terms of two major clusters: one of identities andanother of attributes. (In the absence of a prime, the order ofthese two clusters would be random.) The integration model,which assumes that social identities are linked to specific sets ofattributes, would predict that clustering patterns would reflectthese distinct cognitive structures. Accordingly, the number ofclusters would vary by participant, with each cluster containinga combination of one or more identities and some number ofattributes.

We analyzed the sequencing of items in recall in terms of anadjusted ratio of clustering (ARC), developed by Roenker,Thompson, and Brown (1971). ARC scores can range from +1to — 1; a score of 0 indicates chance clustering. Furthermore,because the value of an ARC score is relatively unaffected bythe number of categories or the total number of categories re-called, it offers a reasonable basis for comparing the predictionsmade from the two models, which rely on different numbers ofcategories.

To test the prediction derived from the segregation model, weanalyzed recall lists in terms of the clustering of items in one oftwo categories: identities or attributes. We calculated an ARCscore for each respondent (N = 52) and then summarizedscores across respondents. The average ARC score was .23(individual scores ranged from -.30 to 1.00). This degree ofclustering is significantly greater than what would be expectedon the basis of chance (z = 4.6, p< .001).

To test the integration model, which is based on idiosyncraticcombinations of identities and attributes, it is necessary to referto the original lists of identities and attributes provided by par-ticipants in the first session. In the first session, respondentslisted their identities and the attributes they associated witheach identity. On the basis of this information, associated items(i.e., an identity plus its linked attributes) were assigned thesame code number, whereas items that were not associated re-ceived different code numbers. Thus, if a person initially indi-cated that his or her identity as a student could be characterizedby intelligence, studiousness, and future-orientation, thosethree attributes would be scored with the same code as student.Intrusions, that is, items appearing in the recall list that were

not originally generated by respondents, were treated as nonoc-currences and ignored in the analysis.

We calculated an ARC score for each respondent (N = 52)on the basis of the number of identity clusters that he or shenamed. Summarized over participants, the average ARC scorewas .34 (individual scores ranged from -.50 to 1.00). This de-gree of clustering is significantly greater than what would beexpected on the basis of chance (z= 11.33,p< .001).

These results provide support for both the segregation modeland the integration model, though the support for the latter isstronger. The fact that both models gained some support is notsurprising, because both models predict that there will be somegrouping on the basis of attributes. For the segregation model,all attributes should cluster together, distinct from the cluster ofidentities. For the integration model, subsets of attributes arepredicted to cluster together, each associated with its own iden-tity category. Thus, the predictions of the two models overlap tosome extent. The stronger results for the pattern of clusteringpredicted by the integration model, however, argue in favor ofthe more complicated pattern of organization.

The analysis of clustering, using a single identity and its asso-ciated attributes as initially generated by respondents, serves asone test of the integration model. However, it does not fully takeinto account the premise of the integration model, namely thatmore than one identity as well as more than one attribute maybe contained within a single cognitive organization. This as-sumption, which requires more detailed representation of iden-tity-attribute associations, is tested by HICLAS analysis.

Hierarchical classes of identity structure. DisjunctiveHICLAS analysis, as developed by DeBoeck and Rosenberg(1988), provides a means of determining, for each individualrespondent, the degree to which identities and attributes are as-sociated with one another. HICLAS is based on the binary ma-trix of association between identities and attributes and pro-duces a hierarchical model of an individual's identity structure.Initially, the investigator chooses the number of ranks for theanalysis, which can be considered the number of blocks at thebase of the resulting pyramid. On the basis of both past researchfindings as well as the goodness of fit to the present data, wechose a Rank 3 solution.4

In the present analysis, we used individual HICLAS outputssuch as the hypothetical one depicted in Figure 2 as the basisfor determining which identities and attributes were associatedwith each other. Identities and attributes were considered to beassociated if (a) they occurred in the same class or (b) theywere in connected classes. As an example of the first case, theHICLAS output in Figure 2 shows that daughter and sister oc-cupy the same class, by virtue of being described by the sameset of attributes. The second case of association derives from thehierarchical assumptions of this particular model. One identityclass may subsume one or more other classes, or it may be sub-sumed by a more general, superordinate class. In Figure 2, thesecircumstances are illustrated by the association of lawyer with

4 The average goodness of fit was .83 and ranged from .52 to 1.0 inindividual solutions; generally, a goodness-of-fit ratio greater than .70is considered acceptable (S. Rosenberg, personal communication, May1992).

SPECIAL ISSUE: SOCIAL AND PERSONAL IDENTITIES 1089

WOMAN

/

/

SISTERDAUGHTER

/

PARTNER

\

\

LAWYER

1VOLUNTEER

RelaxedImmatureSpoiled

HardworkingSuccessful

\

\

\

Smart

\

Concerned

1ActiveConfident

Figure 2. A hypothetical hierarchical classes analysis output. Identi-ties appear in uppercase letters; attributes appear in upper and lower-case letters.

the superordinate class (woman) as well as with two subordi-nate classes (partner and volunteer).

On the basis of these coded associations, we analyzed the or-der of recall of items in terms of probabilities, similar to ourfirst analysis and that of Trafimow et al. (1991). Rather thanconsidering only whether items were identities or attributes,however, we also considered whether they were associated(matched) or not associated (unmatched) with one another.Thus, a matched attribute-identity (MA 11) pairing is one inwhich an attribute follows an identity with which it is associ-ated, that is, contained within the same class or related superor-dinate or subordinate class. An unmatched attribute-identity(UA11) pairing is one in which an attribute follows an identitywith which it is not associated. In all, we coded for three typesof sequences: matched attribute-attribute pairings (MA|A),matched identity-identity pairings (MI 11), and matched attri-bute-identity or identity-attribute pairings (MA|I or MI | A).Obtained mean probabilities for each of these paired sequencesare shown in the second column from the left in Table 2.

To determine whether these obtained probabilities weregreater or less than what would be predicted by chance alone,we computed base rates of association from each participant'sHICLAS solution. We accomplished this by calculating the pro-portion of all possible identity-identity, attribute-attribute, andattribute-identity (identity-attribute) pairings that would havebeen coded as matched had they been paired sequentially inrecall. The mean probabilities for each of the paired sequences

expected on the basis of chance alone are shown in the thirdcolumn from the left in Table 2.

When one compares the probabilities obtained from respon-dents' recall lists with those expected on the basis of HICLASalone it becomes apparent that association, in addition to typeof self-cognition (identity or attribute), plays a role in the stor-age of self-relevant information. Respondents recalled matchedattribute-attribute pairings at levels well above the base rate,F(l, 25) = 6.58, p = .02. Similarly, when two identities wererecalled together, they tended to be matched at a rate greaterthan that predicted by chance, F( 1,25) = 3.11, p = .09. Finally,although matched attribute-identity and identity-attributepairings were not significantly more likely than would be pre-dicted by chance alone, the means were in a direction consistentwith the integration model.

Discussion

A segregation model of self-representation, such as thatoffered by Trafimow et al. (1991), posits that social identitiesand personal attributes are represented separately in memory.According to this model, the social identities that an individualhas are linked together, and attention to or recall of one of thesecollective representations will facilitate thought and recall ofother social identities. Stored separately are personal attributes,which also are linked among themselves in thought and mem-ory. Although Trafimow et al. (1991) speculated that furthersubdivisions could occur within each of these two self-struc-tures—for example, there could be subsets of personal attri-butes such as musical-related traits—they were clear in theirposition that "the distinction between private and collectiveself-cognitions is both meaningful and important" (p. 653).

Although we do not deny the conceptual distinction betweensocial identities and personal attributes, we suggest that an inte-gration model, in which identities and attributes are inextrica-bly linked to one another, offers a better way to conceptualizethe organization of self-structure. From this perspective, attri-butes serve to give meaning to the social categories to whichpeople belong. In claiming a social identity, people use attri-butes, traits, and behaviors to say what the category is and whatit means to be a member of the category. Thus the integrationmodel suggests that self-structure consists of a set of identityclusters, each of which may contain one or more identities andattributes. Although more elaborate in its analysis of cognitive

Table 2Mean Conditional Probabilities of Obtained and ExpectedIdentity-Attribute Pairings

Type of item pairing

Obtained Expected

Probability M SD M SD F(l,25)

p(MA|A) .63 .28 .56 .29 6.58 .02p(MI|I) .54 .40 .44 .23 3.11 .09p(Ml|A)andp(MA|I) .59 .26 .55 .25 1.18 .29

Note. M = matched; A = attribute; I = identity.

1090 REID AND DEAUX

structure than earlier models of multiplicity, the integrationmodel certainly shares assumptions with early discussions byJames (1890/1950) and Mead (1934/1962), as well as withmore recent versions of role theory and identity theory (Stryker&Statham, 1985).

The two positions are not totally incompatible. Attribute-attribute linkages are expected in both models, for example, butthe extent and specification of these linkages differ in the twomodels. Whereas the segregation model predicts that all attri-butes will be linked and distinguished from all identities, theintegration model predicts with more specificity which attri-butes and which identities might be linked. In addition, the in-tegration model is unique in predicting specifiable links be-tween identities and attributes. Given some areas of overlap, itis perhaps not surprising that the data reported here providesome support for both positions. Although we replicated theresults of Trafimow et al. (1991), the overall pattern of resultsprovides stronger support for an integration model of self-representation.

In the first analysis of conditional probabilities, we found, asdid Trafimow et al. (1991), that attributes are more likely to belinked to other attributes and identities to other identities. Yetthese patterns, though statistically different from a chance dis-tribution, are also at a considerable distance from a perfect fit toa two-basket model. Such a disparity suggests that an alternativerepresentation is needed, in which the linkages predicted by thesegregation model are only part of a more complicated storyand in fact may be the by-product of different organizationalstrategies.

Additional analyses provided support for this alternative po-sition. In the first test, we considered the overall pattern of clus-ters in recall and tested a two-basket model against our hypoth-esized multiple-basket model. Because the predictions of thetwo models partially overlap, as noted above, it is not surprisingto find that the clustering analysis provides some support forboth strategies. However, the results were stronger for the moredifferentiated integration model, in which clusters are idiosyn-cratic combinations of identities and attributes.

This supportive pattern of results occurs even though theanalysis of clusters does not take into account all of the premisesof the integration model. Specifically, the integration model as-sumes that identities can be conceptually linked to other identi-ties as well as to attributes. In the clustering analysis, attributescould be linked only to a single identity, reflecting the initialstatements of the respondent.

The second test of the model, in which HICLAS techniquesare used, makes possible a more sensitive definition of the indi-vidual self-structures, permitting one to chart the overlap be-tween some identities and attributes in a single cluster. In thiscase, the predictions are unique to the integration model. Thesupport of these predictions, in contrast to a null hypothesisderived from the segregation model, further argues for the morecomplex representation suggested by the integration model.

An integration model does not deny the utility of distinguish-ing personal attributes from social identity in operational terms;rather, it stresses the need to consider the ways in which thesetwo aspects of the self interrelate. The distinction between so-cial and personal identity is common within the theoretical tra-dition of social identity and self-categorization (Hogg & Ab-

rams, 1988; Turner et al., 1987). However, although this dis-tinction is sometimes described in terms of a difference in thecontent of self-description, it more appropriately characterizesa difference in emphasis or focus: on the one hand considerationof the ways in which one is different from other people, and onthe other hand an emphasis on the commonality of collectivemembership (Deaux, 1996). Turner et al. (1994) specificallymade the point that it is not particular attributes that distin-guish personal from social identity, but rather the kind of com-parison that is being made with that dimension. Personal attri-butes can be used either to describe how one is similar to othersin a social category or to highlight one's unique characteristics.Thus, it is important to distinguish between structure and pro-cess when using these common terms.

Although we argue that personal attributes provide meaningto social categories, we do not claim that clusters of attributescannot exist apart from any social category. Indeed, theHICLAS analysis that we used allows for groups of attributesthat have no one-to-one correspondence to a social category.Certain personality traits may also occupy a superordinate po-sition, essentially characterizing all social identity categoriesand hence not serving to differentiate among them. A charac-teristic such as honesty, for example, might be the kind of traitthat would inform all aspects of self-relevant behavior. (Evenhere, however, one can think of counterexamples, such as onein which honesty might be linked to a religious identity but notincluded in a work identity.)

It also is possible to consider social identities that are notgiven meaning by personal attributes. When one first claims agroup membership, for example, a lack of experience maymean that for some period of time the social identity exists with-out much personal meaning. Available social representationsmight help define the category in some general terms, but thesecharacteristics might not immediately be incorporated into theself-structure.

Just how attributes and social identities come to be linkedtogether is an important issue for research. The relative weightthat cultural representations have in defining a social identity,versus the impact of individual experience, is a question worthpursuing. So, too, are issues of change in self-representation:How fluid are the attribute-identity linkages over time? Ques-tions of self-structure are often framed in static terms, but suchstructures are surely subject to modification as a function ofchoice or circumstance.

Many other issues related to self-structure remain to be pur-sued. In considering the overlap between various identities, forexample, one might question whether certain types of socialidentity are more likely than others to share attributes. An anal-ysis of identity clusters by Deaux, Reid, Mizrahi, and Ethier(1995) suggest several different types of social identity, charac-terized by different psychological dimensions and including dis-tinct exemplars. Perhaps identities that are part of the samecluster are more likely to overlap in self-structure, depending onsimilar kinds of meanings and attributes to characterize them.If so, approaches to diagnosing self-structure that are morenomothetic than the ones presented here might be possible.

At the same time, the methods we used here focus on individ-ual patterns of association and offer important tools for theanalysis of self-structure which is, as most theorists assume,

SPECIAL ISSUE: SOCIAL AND PERSONAL IDENTITIES 1091

idiosyncratic in its development and form. Only with such anapproach is it possible to reveal the kinds of associations that anintegration model proposes and to explore the critical questionsof meaning that a segregation model cannot address.

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Received April 14,1995Revision received November 16, 1995

Accepted November 26, 1995 •