Generality and topic specificity of cognitive styles

JOURNAL OF RESEARCH IN PERSONALITY 9, 366-374 (1975)

Generality and Topic Specificity of Cognitive Styles

CHRISTOPHER PETERSON AND WILLIAM A. SCOTT

University of Colorado

A multitrait-multimethod strategy was used to assess eight structural properties of cognition applied to several classes of objects by 88 university students in Boulder, Colorado, United States, and 80 university students in Kyoto and Otsu, Japan. Each cognitive style was found to display some degree of generality over object classes and also some degree of class specificity. It is concluded that cognitive style depends on the subject, the class of objects considered, and an interaction between the two.

For the most part, theory and research on cognitive styles have followed the assumption that individual differences in modes of thinking represent stable and characteristic traits and are displayed consistently over a wide range of situations. Thus, for example, Gardner (1953, p. 229) defines equivalence range as a consistent individual difference in what is accepted as “similar or identical in a variety of adaptive tasks.” Pettigrew (1958, p. 543) conceives of category width as an individual consistency in estimating “the extremes of a number of diverse cat- egories-from length of whales to rainfall in Washington, D.C.” Harvey, Hunt, and Schroder (1961) describe varying degrees of ab- stractness and concreteness as pervasive individual tendencies. An alter- native view, that styles of thinking depend substantially on what the person is thinking about, has not received much systematic attention but has been alluded to by Scott (1969).

There is no inherent conflict between the two perspectives, as styles of thinking may depend on both the person and subject matter. Scott (1974b) has shown that the typical mode of cognitive integration depends on the types of objects considered. One may also inquire about the interaction between person and cognitive domain. To the extent that cognitive styles reflect such an interaction, they will be more consistently displayed within some domains than others, and the choice of domain will vary from person to person.

This research was supported by Grant No. MH-07998 from the National Institute of Mental Health. The authors are indebted to the following persons for facilitating the research in various ways: Ruth Scott, Kay Blight, Shigeo Imamura, Satoru Inomata, and Okichi Endo. Mr. Scott is now at Department of Behavioural Sciences, James Cook Uni- versity, Townsville, 48 10, Australia, to which address requests for reprints should be sent.

366

Copyright @ 1975 by Academic Press, Inc. Au riphts of reproduction in any form rcservcd.

COGNITIVE STYLES 367

HYPOTHESES

In order to ascertain the degree to which a particular cognitive style is general or domain specific, it is necessary to have several measures of a cognitive style, each referring to a single domain of objects. If every domain is represented by several instruments, then one hypothesis of domain specificity may be formulated as follows:

Hypothesis 1

Measures of a given cognitive style within a single domain of cognition are more highly correlated with each other than they are with comparable measures from a different cognitive domain.

Another hypothesis of domain specificity may be developed from theories about interrelations among cognitive styles. If two cognitive styles are theoretically related, then:

Hypothesis 2

The correlation between two theoretically related cognitive styles is higher within a single domain of cognition than between two different cognitive domains.

Finally, if a cognitive style is theoretically relevant to an external variable that pertains to a particular domain of events, then:

Hypothesis 3

The correlation of an external variable with a cognitive style measured from the same domain of events is larger than its correlation with the same cognitive style measured from a different domain.

These hypotheses were tested with data from two cultures (United States and Japan) pertaining to cognitive styles and personal maladjustment.

METHOD

Subjects Subjects in the United States sample were 88 students at the University of Colorado

who were recruited from two different sources. From the University Counseling Service came 34 subjects; from responses to advertisements in the school newspaper came 54 other subjects. Subjects in the Japanese sample were 80 undergraduate psychology students at Doshisha and Shiga Universities. All subjects filled out questionnaires pertaining to personal maladjustment. The United States sample filled out questionnaires pertaining to six different cognitive domains (acquaintances, family, societal groups, nations, school, and self). The Japanese subjects filled out questionnaires pertaining to only four domains (acquaintances, family, nations, and self).

Measures of Maladjustment

Personal maladjustment was measured by (a) Areas of Dissatisfaction wifh Self (15 items related to the self-concept rated on a 5-point scale of dissatisfaction), (b) My Effect

368 PETERSON AND SCOTT

on Others (10 items, related to the subject’s social attractiveness, with which he agreed or disagreed, (c) Manifest Anxiety (a short form of Taylor’s, 1953, scale), and (d) Dislike of Self-Roles (10 common roles rated on a 7-point scale of dislike). These measures had high intercorrelations. For the analyses reported here, a composite maladjustment measure, based on equally weighted scores, was formed. Details of these procedures may be found in Crabbe and Scott (1972) and Scott (1974a).

Measures of Cognitive Styles Cognitive styles were measured by 10 different questionnaires for each cognitive do-

main. The questionnaire formats, similar across domains, were (a) Listing and Grouping of 20 volunteered objects, (b) Free Description and Rating of 10 supplied objects, (c) Checklist Description of 10 supplied objects with 72 supplied adjectives, (d) Free Compar- ison of five pairs of objects, (e) Checklist Comparison of five pairs of objects with 72 supplied adjectives, (fJ Grouping of 30 supplied objects on 10 attributes, (g) Most Similar Pairs of 20 supplied objects, (h) Similarity of 20 supplied pairs of objects, (i) Homogenizing of eight supplied sets of seven objects each, and (j) Rating of 20 supplied objects on 10 bipolar attributes. Many of the questionnaires required that the subject indicate his liking for the various objects on a 7-point scale.

The order of administration was constant for all subjects in a sample; it was determined in such a way as to (a) present free-response questionnaires first, (b) alternate domains, and (c) alternate formats. Thus, instruments for the several cognitive domains were in- termingled throughout the test session.

A multitrait-multimethod procedure (Campbell & Fiske, 1959) was used in measuring properties of cognitive style from responses to these questionnaires. Each property was assessed with several instruments. In most instances, each instrument was used to assess several properties. Table 1 indicates which questionnaires were used to measure the various properties. For the analyses reported here, composite measures for each property within each domain were formed by summing equally weighted scores from the appropri- ate questionnaires. Details of these procedures may be found in Scott (1969, 1974a, 1974b).

Two types of cognitive complexity were measured. Dimensionality refers to the number of independent attributes that a subject uses to describe objects in a given domain. The

TABLE 1 QUESTIONNAIRE FORMATS USED TO ASSESS COGNITIVE PROPERTIES

Format&

Property a bcdefghij

Dimensionality Precision Affective balance Image comparability Centralization Affective-evaluative

consistency Evaluative centrality Ambivalence

x x x x x x X

X X

X x x x x

x x x x x x

x x x x

x x X X

x x X

x x X

a The format letters refer to measures of cognitive styles as identified in the text.


precision of an attribute is the number of distinctions that a subject makes along that attribute; an average score is computed over all attributes represented in a particular instrument.

Four styles of cognitive integration were measured. Centralization is a subject’s tendency to describe all objects with a single attribute. Image comparability is a subject’s tendency to describe all objects with the same large set of attributes. A$ective balance is the degree to which a subject classifies objects on all attributes in terms of how much he likes those objects. Affective-evaluative consisrency is a subject’s tendency to evaluate objects in accordance with his liking for them.

Two additional properties of cognitive style were measured for each domain. Ev&arive cenirality is a measure of a subject’s use of predominantly evaluative attributes. Am- bivalence is the degree to which a subject describes each object with both favorable and unfavorable adjectives.

Example

These methods may be illustrated by considering a typical questionnaire format, Checklist Comparison, from which scores for dimensionality, image comparability, and centralization are obtained. This questionnaire consisted of five pairs of objects from the domain under consideration. For each pair, a subject was asked to employ an adjective checklist consisting of 36 attributes (i.e., antonym pairs relevant to the description of objects in the specific domain) to specify similarities and differences.

Dimensionality was assessed by counting the total number of attributes employed by the subject (maximum = 36) to describe ah similarities and differences over the five pairs of objects. Image comparability was scored by the formula

COMP = 5 n”-/I(k + l), j=1 nt-- 1

where n, is the number of object pairs assigned to any attribute used once and nt is the total number of object pairs in the instrument (in this case, five). These proportions are summed over the k antonym pairs (maximum = 36) used by the subject. Finally, centralization was scored as CENT = p,, - M,*, Hhere ph is the proportion of object pairs assigned to the most used attribute. (This represents the centrality-the proportion of all objects assigned to an attribute-of the most central attribute in the domain.) M,* is the mean centrality of all other attributes used by the subject.

RESULTS

Hypothesis 1

The first question investigated was whether measures of a given cognitive style within a single domain of cognition are more highly correlated with each other than they are with comparable measures from a different domain. The simplest tiay of testing this hypothesis is to compare the reliabilities of the measure of a property within a domain (from Cron- bath’s, 195 1, coefficient alpha) with the correlations of that property’s composites across domains. Accordingly, when the composites were formed for each property within each domain, the reliability of each composite was assessed. Then interdomain correlations for each prop-


TABLE 2 MEAN RELIABILITIES AND INTERDOMAIN CORRELATIONS OF COGNITIVE PROPERTIES

Property Sample: United States Correlation: 7,x’” Pm”

Japan rxx TXY

Dimensionality Precision Affective balance Image comparability Centralization Affective-evaluative

consistency Evaluative centrality Ambivalence

Mean

.60 .55

.24 .13 -29 .26 s9 .53 .33 .22

.31 .19 .53 .21 SO .41 .49 .38 Sl .20 .45 .28 .42 .31 .46 .30

A7 .29 .41 .33 .48 .14 .58 .43 .29” .30

a f, is the mean reliability estimated by Cronbach’s (1951) coefficient alpha. bP xy is the mean cross-domain correlation corrected to eliminate the effect due to

mon instrument formats (see Peterson, 1974). c Exception to topic-specificity hypothesis.

erty were calculated by a multimethod correlation procedure that corrected for the effects of common instruments.’

Table 2 summarizes these results2 The mean reliability for each property in the United States sample was based on the reliabilities of all six domains assessed. In the Japanese sample, it was based on the reliabilities of all four domains assessed. The mean interdomain correlation for each property in the United States sample was based on 15 cross- domain correlations. In the Japanese sample, it was based on six cross- domain correlations.

1 The formula for the multimethod correlation coefficient is

where X is a composite of m standardized items with standard deviation ox, Y is a composite of n standardized items with standard deviation oy, c is the number of similar formats in the two composites, x, and y, are items from nonsimilar formats, and N is the number of subjects. This formula estimates the correlation between composite scores X and Y on the assumption that correlations between two traits measured by the same instrument format equal the average correlation between the same traits measured by different instrument formats (see Peterson, 1974).

z The full sets of correlations summarized in Tables 2 and 3 have been deposited with the American Society for Information Science. Order NAPS Document No. 02594 from ASIWNAPS, c/o Microfiche Publications, 440 Park Avenue South, New York, N. Y. 10016. Remit in advance $1.50 for microfiche or $5.00 for photocopies. The full set of correlations summarized in Table 4 is reported by Scott (1974a).


The mean interdomain correlations are consistently positive and average .30 over the eight cognitive styles in the two samples. In all but one instance (out of 16) they are exceeded by the mean reliabilities of the same properties, which average .44. Accordingly, it may be concluded that cognitive style is to some degree topic specific and to some degree similarly manifest over the four or six domains represented here.

Hypothesis 2

The second question investigated was whether theoretically predicted relationships between cognitive styles are stronger within a single domain of cognition than between different domains. Table 3 summarizes 12 hypothesized relationships (rationales for which appear in Scott, 1969, 1974b) and the mean correlations when the relationships are tested within domains and when they are tested across domains. For instance, the first relationship-that dimensionality is negatively related to affective-evaluative consistency -was tested six times within domains for the United States sample and four times within domains for the Japa-

TABLE 3 MEAN CORRELATIONS BETWEEN COGNITIVE PROPERTIES WITHIN AND

BETWEEN DOMAINS

Hypothesized relationship Sample: United States Japan Correlation: 7,” ‘I,* Fw I’ I,

Dimensionality -r affective- evaluative consistency

Dimensionality -r balance Dimensionality r ambivalence Dimensionality r centralization Dimensionality r image

comparability Dimensionality r precision Affective-evaluative

consistency r balance Affective-evaluative

consistency -r ambivalence Affective-evaluative consis-

tency r evaluative centrality Balance -r ambivalence Balance r evaluative centrality Image comparability r precision

Meat-P

-.24 -.19 -.04 .04 -.30 -.25 -.lO - .02

.35 .25 .32 .16 .18 .I6 .21 .I8

.3-l’ .38 .39 .34

.31 .29 .27 .24

.39 .16 .32 10

-.31 -.lO

.16 .ll .27 .I9 -.17 -.ll - .28 -.lO

.17 .16 .14 .08

.24 .21 .22 .I8

.27 .20 .25 .14

- .44 -.I7

n f,,, is the mean predicted correlation within domains. * P,, is the mean predicted correlation between domains c Exception to topic-specificity hypothesis. d Mean calculated on absolute value of correlations.


TABLE 4 MEAN CORRELATION BETWEEN ADJUSTMENT AND COGNITIVE PROPERTIES FOR

RELEVANT AND IRRELEVANT DOMAINS

Sample: United States Property Correlation: Jp ria

--c Evaluative centrality .25 .08 Affective-evaluative

consistency .24 .03 Ambivalence -.37 .05

n P, is the mean predicted correlation with relevant domains. a r, is the predicted correlation with irrelevant domain.

Japan fr Ti

.03 - .02

.15 .04 -.34 -.20

nese sample. It was tested 30 times across domains for the United States sample and 12 times across domains for the Japanese sample.

The mean cross-domain correlations are typically in the predicted directions; in all but one instance (out of 24) they are exceeded by the corresponding mean within-domain correlations. Again, it may be concluded that cognitive style is to some degree similarly manifest over different topics.

Hypothesis 3

The third question investigated was whether relationships between external variables and cognitive styles are stronger when the cognitive style is measured from a theoretically relevant domain than when measured from a theoretically irrelevant domain. Scott (1974a) hypothesized that personal adjustment is positively related to evaluative centrality, positively related to affective-evaluative consistency, and negatively related to ambivalence when these properties are assessed from personal domains (acquaintances, family, and self) but that no relationships are present when these properties are assessed from impersonal domains (nations). Table 4 summarizes the mean correlations between measures of adjustment and these cognitive styles as assessed from relevant and irrelevant domains in the present samples.

In no instance (out of six) do the mean correlations with irrelevant domains exceed the mean correlations with relevant domains. For pur- poses of this hypothesis, it may be concluded that the impact of cognitive style is topic specific rather than general across all cognitive domains.

DISCUSSION

The bulk of the data reported here supports the conclusion that cognitive style is to some degree topic specific and to some degree generally manifested over the several domains represented. The obtained levels of


topic specificity do not seem high enough to account for the typical find- ing of low intercorrelation among measures of a particular cognitive style with tasks of heterogeneous content (e.g., Gardner & Schoen, 1962; Kenny & Ginsburg, 1958; Sloane, Gorlow, & Jackson, 1963; Vannoy, 1965). Therefore, additional bases for the low intercorrelations should be sought in task-specific response sets and in poorly concep- tualized cognitive styles.

Unfortunately, the present study did not sample enough domains to allow any firm conclusion about which cognitive styles generalize the most and which domains are most similarly cognized. A rough impression from the data is that the affective properties (evaluative centrality, affective balance, affective-evaluative consistency, and ambivalence) are less general than the nonalfective properties (dimensionality, precision, centralization, and image comparability). This result is clouded, of course, by differential reliabilities of the measures, but it is compatible with other work in personality assessment (see Mischel, 1968, for a review) that reports the greatest cross-situational generality for measures of intellectual ability. In the United States sample, grade-point average and SAT scores were substantially related to the nonaffective styles but not to the affective styles.

Another rough impression is that properties assessed from “similar” domains (e.g., acquaintances and family) correspond more highly than properties assessed from “different” domains (e.g., acquaintances and nations). While this is a reasonable idea, it is difficult to specify just what it is about cognitive domains that defines their similarity or difference. Indeed, there are probably individual differences in the degree to which two domains are cognized similarly, and there are probably individual differences in the parameters used to appraise domain similarity. These differences are research problems in and of themselves. For instance, does the tendency to think about groups as if they were people have any relationship to leader (or follower) behavior? Does the tendency to con- ceptualize self and acquaintances similarly relate to adjustment?

An assumption of generality of cognitive styles has been common in psychology. The present data have confirmed this assumption to a con- siderable degree but have also qualified it with the observation that styles of thinking depend also on the domain of thought and on an interaction between person and domain.

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Generality and topic specificity of cognitive styles