measuring attitudes toward self and others in society: state of the art

Measuring Attitudes toward Self and Others in Society: State of the ArtAuthor(s): Karl Schuessler and Larry FreshnockSource: Social Forces, Vol. 56, No. 4 (Jun., 1978), pp. 1228-1244Published by: Oxford University PressStable URL: http://www.jstor.org/stable/2577520 .

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Measuring Attitudes Toward Self and

Others in Society: State of the Art*

KARL SCHUESSLER, Indiana University L A R R Y F R E S H N O C K, Indiana University

ABSTRACT This study investigates the correlation between 31 social life tests

within and across groupings by title, by concept, and by factor weights; also the principal dimensions of the 107 items appearing in those tests. Unadjusted test correlations tend to be larger within groupings than across them; however, correlations adjusted for overlapping items show little or no difference within and between groupings. Item correlations contain eight significant (as defined) dimensions; corresponding factor scores were interpreted to be measures of pessimism, depression, cynicism, anxiety, fatalism, job morale, life satisfaction, and personal morale. The findings on the whole suggest that the number of social life attitude tests in use in sociology is excessive and that a smaller number would suffice.

Since the 1930s sociologists have constructed a number of objective tests to measure attitudes and feelings toward self and others in society. These tests go by different but related titles: morale, normlessness, estrange- ment, etc. Since they contain closely similar items, by that criterion they all belong to the same domain. Consequently, one would expect all tests to be positively correlated with one another but not necessarily to the same degree. Tests bearing the same title might be expected to be more closely correlated than tests bearing different titles; tests representing the same construct might be expected to be more closely correlated than tests representing different constructs.

This study addresses these issues by analyzing first the correlations between 31 separate tests-hereafter referred to as the tests-and second the correlatons between the 107 items appearing in those tests. It seeks to answer the following specific questions: (1) Do tests with the same title correlate more closely than tests with different titles? (2) Do tests purporting

*This research was supported directly by a U.S. Public Health Service Grant (MH22294) and indirectly by a U.S. Public Health Service Training Grant (MH10577). We wish to acknowl- edge our debt to Norman Bradburn, Melvin Kohn, and Melvin Seeman, who read a prelimi- nary draft of this paper and made suggestions for its improvement; also our debt to Robin Stryker for her careful examination of writings pertaining to test constructs.

1228



Measuring Attitudes / 1229

to measure the same construct correlate more closely than tests purporting to measure different constructs? (3) Do tests grouped by a statistical criterion correlate more closely than tests grouped by either title or construct? (4) Do the test items appearing in these statistical groupings express a single underlying trait? (5) Do factor scores based on all 107 items shed light on what it is the 31 tests are measuring?

These questions direct attention to the possibility that some replica- tions may be that in name only, either because tests of ostensibly the same trait are negligibly correlated or because tests of ostensibly different traits are substantially correlated. They are also concerned, at least indirectly, with the possibility of eventually replacing many special tests by a few general ones, and with statistical devices that might be used to accomplish that objective.

Answers to the questions listed above are based on the responses of a U.S. probability sample of 1,522 adults.1 The survey questionnaire con- sisted of 237 items drawn from over 100 tests for measuring attitudes about self and others in society-what we shall refer to as social life tests.2 For purposes of this analysis, a test was included if it was represented by at least three items; correspondingly, items were dropped if they were not matched by at least two items from the same test. Eliminating tests with fewer than three items, left us with 31 tests; dropping items not appearing in at least one of these tests, left us with 107 items.

Table 1 gives selected information about the 31 tests: author and title, date, number of items, number of items available for analysis. Ideally, none of the items in the 31 tests would have been excluded from the survey questionnaire and no test would have been incompletely represented.3 However, practical restrictions on the length of the sample survey schedule made this impossible. Items were selected primarily to reflect the major subdivisions of our domain and only secondarily to represent the given tests from which they were taken, although an effort was made to satisfy both criteria whenever possible.

Related Research

The dimensionality of some of the 31 tests, singly or in groups, and/or combinations of items appearing in those tests, has been considered in previous studies. We cite here only those studies that are quite similar in method and purpose to ours.

Neal and Rettig (a) investigated the relationship between normlessness, powerlessness, and anomia. From their findings, based on the replies of 603 residents of Columbus (Ohio), they concluded that powerlessness and normlessness are "orthogonal to Srole's anomie scale." Later (b) they reanalyzed their data taking into account some methodological sugges-



1230 / Social Forces / vol. 56:4, june 1978

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tions made by Cartwright; this reanalysis required a modification of the conclusion that alienation and anomia are uncorrelated.

In a similar inquiry, Struening and Richardson studied the relationship between alienation and anomia. Their findings rest on the responses of 422 institutionalized subjects (prisoners, inmates, etc.) to a questionnaire consisting of 68 items on alienation and related concepts. In the course of their analysis, Srole's anomia scale became attached to their first factor scale, which they named "Alienation via Rejection," with the implication that anomia is an aspect of alienation.

Dodder administered Dean's Alienation Scale to 201 adult women living in five Kansas communities with a view to establishing its dimensionality. He concluded that this questionnaire, contrary to Dean's claim, does not measure powerlessness, normlessness, and social isolation but rather "unanticipated latent dimensions." He interpreted the statistically strongest of these dimensions to be retreatist alienation.

In somewhat the same spirit, Gurin and associates examined the dimensionality of the 23 items comprising Rotter's Internal-External Con- trol Scale. Their major conclusion, based on a sample of 1,212 black college students, was that Rotter's scale is multidimensional, and that items related to expectations for self (personal control) and items related to beliefs about others (ideology control) should be scored separately.

In more recent work, Cherlin and Reeder have considered the dimensionality of the Bradburn-Caplovitz Affect Balance Scale. They gave this questionnaire in 1972 to 1,078 adults living in Los Angeles County, and to a different sample of 1,008 adults in the same county in 1973. Their major finding, anticipated by Bradbum's less intricate statistical analysis, was that the Affect Balance Scale reflects two different dimensions, and that these might be scored separately.

The studies cited above are similar to ours in their concern with the dimensionality of tests, and/or subsets of items appearing in those tests. They differ from ours in scope: they are based on fewer tests and fewer items, and smaller and less heterogeneous populations.

Classification by Title

We considered first the null possibility that tests with different titles were no less closely correlated than tests with the same title.4 Our method was to compare the mean correlation between tests bearing the same title- what might be called an internal average-with the mean correlation between these same tests and tests bearing different titles-an external average. Contrary to first impressions, titles did not lend themselves very readily to grouping. Some titles appeared more than once, some appeared only once; some titles were synonymous with another, other titles had no synonym.




Our final classification, shown in Table 2 shows five titles, or what we took to be a synonym, with 5 tests each, and a single category for miscellaneous, or unclassifiable, titles with 6 cases. (For the title grouping of each test, see Table 1.)

The statistical details of Table 2 show that nominally equivalent tests have higher correlations generally than nominally different tests, although the difference varies from one comparison to another. Before correcting for overlapping items, the average of all correlations between tests with the same title is appreciably larger than the average of all correlations between tests with different titles: .516 to .326 for a difference of .190. After correction,5 the overall internal average is only slightly larger than the overall external average: .358 to .317 for a difference of .041. Since there is practi- cally no difference between unadjusted and adjusted external averages (.326 and .317), the initial advantage of the internal average is attributable almost wholly to the presence of overlapping items.

Table 2. MEAN UNADJUSTED AND ADJUSTED CORRELATIONS WITHIN AND BETWEEN TITLE GROUPINGS

Unadjusted Adjusted

Group No. Title Frequency Within Between Difference Within Between Difference

1 Alienation 5 .504 .317 .187 .362 .331 .031

2 Anomia 5 .685 .355 .330 .341 .361 .020

3 Morale 5 .426 .382 .044 .411 .346 .065

4 Control 5 .459 .318 .141 .337 .309 .028

5 Mental well- 5 .486 .300 .186 .341 .282 .059 being

6 Miscellaneous 6 .162 .292 -.130 .139 .278 -.139

C C 31 .516 .326 .190 .358 .317 .041

Mean correlation between tests with same title.

Mean correlation between tests with different title. c

Excluding miscellaneous.

The main point of Table 2 is that, while tests with equivalent titles tend to be more closely associated than tests with different titles, the degree of that association is only moderately large at best. An implication is that research findings based on different, although nominally identical, tests will not necessarily be mutually consistent and may even be contra- dictory. Thus, two tests both named "morale" may produce conflicting




findings on the effect of some situational factor (unemployment, housing) on individual morale. Such conflicting evidence would be quite consistent, however, with our finding that tests bearing the same name may be only weakly correlated with one another.

Classification by Construct

It may be argued that little or no importance should be attached to the relatively weak correlations between nominally equivalent tests since names (titles) are just that and nothing more. On the other hand, a parallel finding for tests purporting to measure the same dimension would be relatively serious, because such tests will be taken by users as exchangeable indices of the same dimension (unless they are forewarned).

To pursue this matter, we undertook to ascertain the construct (concept) underlying each of the 31 tests with the idea of comparing mean correlations for tests of same and different constructs, in the manner of Table 2. Identifying constructs was not a simple task. In some cases, authors had little or nothing to say about their base concept. In other cases, authors seemed to subscribe to the view that the construct behind a test is whatever the items in that test are measuring.

For these and related reasons, our classification of tests by construct, given in Table 3, is more subjective and perhaps more debatable than our classfication by title. (See Table 1 for construct grouping of each test). We recognize that different analysts reviewing the same textual writings might very well come up with different arrangements of tests. Nevertheless, because of the care we exercised, we have some confidence in the reliability of our classification.

Table 3 gives detail corresponding to Table 2 and permits correlations within constructs to be compared with correlations across them. Internal averages are appreciably larger than external averages: before correcting for overlapping items, construct-specific differences range from .083. to .248; the overall difference is .160 (Col. 6). After correction, differences are smaller mainly because the internal averages are smaller: specific differences between mean correlations range from .001 to .126; the difference between overall mean correlations is now .078 (Col. 9).

The implication of Table 3 is that findings based on two or more tests purporting to measure the same trait should b^e compared and merged with caution, since findings based on them will depend more on elements specific to'each test than on elements common to all. To be more concrete: if the correlation between two tests of, say, life satisfaction is .50, and the correlation between one of these tests and annual family income is .60, then the correlation between the other test of satisfaction and income may take any value between -.39 and +.99 (Yule and Kendall). Investigators




Table 3. MEAN UNADJUSTED AND ADJUSTED CORRELATIONS WITHIN AND BETWEEN CONSTRUCT GROUPINGS

Unadjusted Adjusted

Group

No. Title Frequency Within Between Difference Within Between Difference

1 Isolation 5 .575 .389 .186 .345 .344 .001

2 Cyncism 3 .664 .416 .248 .466 .346 .120

3 Demoralization 3 .523 .432 .091 .514 .388 .126

4 Anomie 2 .388 .305 .083 .388 .303 .085

5 Efficacy 5 .459 .307 .152 .337 .307 .030

6 Mental well- 6 .518 .287 .231 .376 .284 .092

being

7 Residual 7 .186 .294 -.108 .222 .277 -.055

31 .526c .366 .160 .391 .313 .078

a Mean correlation between tests purporting to measure same construct.

b Mean correlation between tests purporting to measure different constructs.

Excluding residual.

using these tests may get conflicting evidence, not because the effect of income on satisfaction is unstable, but rather because the tests are so heavily weighted by their disturbance terms.

Classification by Varimax Loadings

Constructs do better than chance in grouping tests, by the criterion that internal averages are generally larger than external averages. Furthermore, it is possible that no comparable arrangement of tests will yield larger differences between such averages.

To appraise this possibility, we made use of factor analysis. Our pro- cedure was to (1) factor the correlations between tests; (2) obtain varimax loadings; and (3) classify tests according to their highest loading. For both adjusted and unadjusted correlations, we set up as many groups as there were factors with a highest loading on at least one test.

The results of this analysis are given in Table 4. Comparing Table 3 and 4, we see that factors do somewhat better in separating correlations than constructs, but not much better. After adjustment, the external average for factors is slightly smaller than the external average for constructs- .296 to .317; however, internal averages are approximately equal- .399 to .391; therefore, the difference between internal and external averages for




Table 4. MEAN UNADJUSTED AND ADJUSTED CORRELATIONS WITHIN AND BETWEEN FACTOR GROUPINGS

Unadjusted Adjusted

Group

No. Frequency Within Between Difference Frequency Within Between Difference

1 11 .684 .323 .361 6 .431 .303 .128

2 5 .493 .302 .191 3 .537 .347 .190

3 4 .530 .245 .285 12 .379 .309 .070

4 5 .490 .253 .237 4 .336 .235 .101

5 3 .614 .304 .310 5 .377 .269 .108

6 3 .342 .284 .058 1 1.000 .330 .670

31 31 .562 .293 .269 .399c .296 .103

Mean correlation between tests from same factor cluster. b

Mean correlation between tests from different factor clusters.

cExcluding Grouping No. 6.

factors is only slightly larger than the difference for constructs-.103 to .078. These statistical findings are the basis of our conclusion that factors are not much better than constructs in segregating correlations.

None of these findings is incompatible with the possibility that tests in a given factor grouping are expressive of a single common trait-low correlations may be accounted for by one factor. To check this possibility, we factored correlations between items within sets. The results, shown in Table 5, are generally against the hypothesis that items within sets have only one trait in common. In only one case (Row 5, Column 8), did the first factor account for more than two thirds of the common-factor variance; in most cases, the fraction was in the vicinity of one half. We would have come to the same conclusion if we had taken the construct (or title) classification as our point of departure.

Factors Scores

The results of the preceding analysis serve mainly to document what might have been foreseen by specialists in the field, namely: that tests within groupings would not be statistically much more similar than tests between groupings; and that items within groupings would not be homogenous in the sense of having only one common trait. Consequently, at this juncture, we turned to factor scores based on the distribution of all 107 items, limiting ourselves to the first eight factors, or to those factors with eigen-



Measuring Attitudes I 1237

Table 5. FACTOR ANALYSIS OF ITEMS WITHIN FACTOR GROUPINGS BY UNADJUSTED AND ADJUSTED CORRELATIONS

Unadjusted Adjusted

No. No. % Common No. No. % Common

Group of of Variance in Group of of Variance in

No. Tests Items First Factor No. Tests Items First Factor

1 11 33 57.2 1 6 35 49.0

2 5 26 49.9 2 3 9 79.7

3 4 21 54.1 3 12 50 36.4

4 5 19 53.6 4 4 21 54.1

5 3 15 48.9 5 5 19 53.6

6 3 9 62.5 6 1 3 31 123* 31 137*

*Some items appeared twice or more.

values larger than 1.00. Our interest in these scores lay in their relation to the tests with which we had started the analysis, and also in whatever conceptual (construct) clarification they might provide.

In line with this interest, and partly to exemplify an approach, we compared interpretations of factor scores based on item correlations with interpretations of factor scores based on test correlations.

For interpretations based on test correlations, each factor score was given the construct (concept) of its statistically closest test, on the assump- tion that test score and factor score are indicators of the same base concept. (The three best tests for each factor are given in Table 6.) This rule, mechanically applied, gave the following concepts for factor scores:

Score Concept Test Correlation

Fl anomia Srole 2 .823 F2 negative self-feelings Bradburn-Caplowitz .801 F3 perceived purposelessness Struening-Richardson .761 F4 worry Bradburn-Caplowitz .713 F5 efficacy in public affairs Smith .695 F6 work morale Walker .617 F7 depressive affect Rosenberg .601 F8 positive self-feelings Bradburn-Caplowitz .578

Several features of this analysis require emphasis: First, assigning constructs to factor scores from best tests is not a demonstration that those scores are measuring those constructs, for at least two reasons: (a) the relation of tests to constructs is uncertain, and (b) the correlations between




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best tests and factor scores are only moderately large (.578 to .823). Second, since some tests are almost tied for first place, we would expect some change in our list of concepts in a different sample. For instance, Langner- Dohrenwend's psychiatric tendency might replace Bradburn-Caplowitz' negative self-feelings in a replication. Moreover, if we had accorded some weight to second and third best correlations, we might have taken social isolation as F3s construct instead of perceived purposelessness. Third, the correspondence between factor scores and best tests is a necessary out- come of the composition of the tests themselves. In starting with internally similar but externally dissimilar subsets of tests, as we did, we necessarily obtain a close correspondence between factors and subsets of tests. (This correspondence suggests incidentally that the 31 tests might be replaced by as few as 8 relatively short tests, constructed so as to account for virtually all of the variance common to the 31 tests.)

For interpretations based on item correlations, we construed each factor score from its statistically closest items, represented in Table 6. For example, we construed Fl as a measure of pessimism, since its strongest items have negative social contingencies as their theme: no ground rules to guide one; no help from others; no social recognition; no future prospects. These items reflect the view that people live to a greater or lesser degree in world of dire and uncertain circumstances. This tendency to emphasize the dark side of things may be regarded as pessimism.

Similarly, we took F5 to be a measure of fatalism in the restricted sense that a person is helpless to change things. It reflects the belief that the individual is acted on by outside forces but exerts very little if any influence on those forces himself. Persons high on this score will tend to discount, if not deny, the influence of the person on both the immediate situation and the larger social world; persons low on this score will assign more weight to individual initiative and effort.

By this method of reasoning, we arrived at the following concepts for factor scores:

Score Concept

Fl pessimism F2 depression F3 cynicism F4 anxiety F5 fatalism F6 job morale F7 life satisfaction F8 personal morale

These interpretations are subject to reservations similar to those based on test correlations. First, they are uncertain because (a) the relation of items to constructs is unknown and (b) the correlations between items and factor scores are generally small. Second, the best items for a given




factor may suggest different interpretations: one item may suggest isolation from others, while another may suggest depression. Different analysts examining the same items would probably come up with somewhat different interpretations of factor scores. Third, the correspondence between factor scores and specific items reflects the tendency of our 107 items to cluster according to the degree of their similarity, as measured by the correlation between them. Since correlations between items and factors rest on the correlations of the items themselves, we anticipate that by changing a few key items, we would change the interpretations of the factor scores.

Comparing the two lists of concepts, we find both agreement and disagreement. Some disagreement was expected, given the way in which interpretations were drawn from item correlations. Since these interpretations were judgmental and arbitrary, there was little reason to expect that they would duplicate interpretations based on best tests.

On the other hand, by either method we would conclude that Factor Scores 1, 3, and 5 are measures of attitudes (feelings) about others in society; whereas Factor Scores 2, 4, 6, 7, and 8 are measures of attitudes (feelings) about self in society.

Since Nos. 1, 3, and 5 refer to attitudes about the social contingencies which people face, they may be of special interest to sociologists. It may be a matter of concern that item and test correlations give rise to somewhat different, although not necessarily conflicting, interpretations. Where item correlations suggest pessimism and cynicism as underlying traits, test correlations suggest anomia and alienation. These discrepancies may be a case of different words with the same meaning, or a case of different meanings with one or the other term used incorrectly. Thus, anomia and pessimism may mean the same thing, or anomia may be the wrong word for Fl, or vice-versa. We suggest that in some cases everyday terms may be more accurate than theoretical language in describing clusters of items.

Summary Points

1. The present study extends previous work on the dimensionality of social life scores in the following respects: (a) covers a longer list of tests and a wider variety of items; (b) samples a larger and more heterogeneous popu- lation; (c) considers the similarity of nominally equivalent tests; (d) considers the similarity of conceptually equivalent tests; and (e) considers whether alternative scorings of the items all at a time provide a useful perspective on, if not necessarily better understanding of, the pattern underlying the responses to the items.

2. After making an allowance for overlapping items, tests with the same name have a mean correlation of .358, compared with a mean of .317 for




tests with different names. This finding confirms what many sociologists would predict from general knowledge but for which there is little or no documentation. However, it is significant chiefly as a reminder that tests with the same name will no more be consistent in their relation to an arbitrary variable than tests with different names, for the reason that components common to all such tests collectively carry less weight than components specific to each one individually. For that reason, a given hypothesis will no more be strengthened by consistent findings than weakened by inconsistent ones.

3. After making a correction for overlap, tests purporting to measure the same construct have a mean correlation of .391, compared with a mean of .313 for tests purporting to measuring different constructs. This finding, as with titles, is significant as a caution against combining evidence from tests purporting to measure the same concept for purposes of testing a given hypothesis. Since the disturbance terms in these tests have more weight than the common terms, they also have more weight in the correlation between these tests and an arbitrary outside variable. Our conclusion is that findings based on tests purporting to measure the same concept cannot be pooled for purposes of testing the same hypothesis.

4. This conclusion holds equally for findings based on tests grouped according to their largest varimax loadings. Tests within factor groupings show more correlation on the average than tests between factor groupings; however, the magnitude of that correlation is moderate at best (.399) after correcting for overlap. Moreover, tests within factor groupings are not homogeneous in the sense that their items collectively have only one trait in common; for most sets of items, the first factor accounts for roughly 50 percent of the common-factor variance (Table 5).

5. For the set of all 107 items, eight factors give fitted correlations that in most cases closely approximate the observed correlations. This finding points to the factor score as a possible approach to the problem of constructing standard attitude measures. It suggests that the 31 tests might be replaced by eight factor scores, since these eight scores represent all that the 31 tests have in common. In raising this abstract possibility, we are not unmindful of the limitations and pitfalls of factor scores (Alwin; Armor,). Factor scores may be quite unreliable-our 0-reliabilities ranged from .755 for Fl to .295 for F8. Furthermore, factor scores-do not supply their own validation. A perfect correlation (in principle) between a factor score and its underlying factor is no demonstration of validity; the problem of validity is not solved by using factor analysis instead of the method of, say, paired comparisons. Nevertheless, such scores hold out the prospect of eventually arriving at a relatively few standard social life scores that will serve the needs of many investigations focusing on the relation of attitudes about self and others in society to social structure and culture.




6. Although the meaning of factors is always uncertain, some of our factors appear to correspond to attitudes about one's own situation, whereas others appear to correspond to attitudes about people in society and community. Factors 1, 3, and 5 reflect attitudes toward man and society; we named them pessimism, social cynicism, and fatalism (partly as a conve- nience). Factors 2, 4, 6, 7, and 8 reflect feelings-depressed, worried, satisfied, pleased-about one's own social situation. In their analysis of Rotter's test, Gurin et al. came to somewhat the same conclusion. All such findings point to the soundness of constructing separate scores for attitudes toward self and society, and attitudes about others; and thereby the means for examining the relationship between them.

Notes

1. Sampling and interviewing was done in 1974 by Response Analysis, Princeton, New Jersey. Details will be furnished upon request. 2. This collection is based on a search of largely sociological writings, 1935-70. Starting with a small number of tests expressing attitudes about self and others in society (e.g., Sletto, Cavan, Srole), we added tests similar in content until the point of diminishing returns was reached. The domain of tests may thus be regarded as a snowball made by crisscrossing the sociological literature and its fringes. 3. Whether the pattern of findings would change with the addition of the missing items is an open question. Our judgment is that this pattern, and, hence, our conclusions would not be significantly different with 100 percent representation of tests. 4. Test correlations will be furnished on request. 5. The correlation between test scores based on overlapping items may be expressed as the sum of the weighted covariances, including the weighted variance of the common items. Re- moving this latter term from the correlation between test scores gives the adjusted coefficient.

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