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no. lO 
 
 GROUP EFFECTIVENESS RESEARCH LABORATORY 
 Department of Psychology 
 University of Illinois 
 Urbana, Illinois 
 
 PROCESSES AFFECTING "UNDERSTANDING OF OTHERS" 
 AND "ASSUMED SIMILARITY" 
 
 LEE J. CRONBACH 
 College of Education, University of Illinois 
 
 TECHNICAL REPORT NO. 10 
 
 Study performed under Contract N6-ori-07135 
 with the Office of Naval Research 
 
 PROJECT ON 
 SOCIAL PERCEPTION AND GROUP EFFECTIVENESS 
 
 AUGUST, 1954 
 
 THE LIBRARY OF thf 
 
 SEP 27 1954 
 
 UNIVERSITY OF lUINOIS 
 

 PROCESSES AFFECTING SCORI]S OH "Ul€)ERSTi.l\IDING OF OTHFRS" 
 AND "ASSUIED SBCLi^-RITY"-'' 
 
 Lee J» Cronbach 
 College of Education, University of Illinois 
 
 How one person judges another is important both for its theo- 
 retical implications and for its practical significance in leadership, 
 clinical assessment, and teaching skill. Recent studies of "social per- 
 ception", as this area is termed, have been chiefly concerned with dif- 
 ferences among perceivers, either in terms of their accuracy, or in terms 
 of their tendency to view others as similar to themselves. 
 
 These studies have usually been built aroimd a particiilar operation 
 in which a judge (J) "predicts" hoi^ another person (o) will respond. 
 
 Often, for example, both persons describe themselves on a personality 
 inventory, and J is then asked to fill out the inventory as he thinks 
 did. The extent to which the prediction agrees with 0*s actual response 
 is talcen as a measure of J's accuracy of social perception (or "empathy", 
 "social sensitivity", or "diagnostic competence", etc). Measurements 
 obtained in this manner are difficult to interpret and several investi- 
 gators have obtained evidence of distressingly low reliability or con- 
 sistency for such scores (11,17,19,28). 
 
 This paper seeks to disentangle some of the many effects xjhich 
 contribute to social perception scores, and to identify separately meas- 
 urable coniponents. This analysis has several results: 
 
 "* Appreciation is expressed to Mary E. Ehart, who assisted in all 
 stages of this paper from initial conception to final interpretation; 
 and to Urie Bronfenbrenner and associates, for helpfully providing data 
 and for their courtesy in exchanging ideas throughout our rather similar 
 inve s tig ations , 
 
 This study was conducted under 01© Contract N6-ori-07135s Fred E, 
 Fiedler, Principal Investigator, 
 
1# It shows that investigators run much risk 
 of giving psychological interpretation to 
 mathematical artifacts, when measures are 
 used which combine the components, 
 
 2, It sheds light on the extent to which adapta- 
 tion to individual differences is advisable, 
 when the differences are not judged accurately, 
 
 3. It directs attention to some especially inter- 
 esting aspects of social perception left untouched 
 by the usual approach. 
 
 Our analysis of social perception scores may also be 
 instructive regarding research strategy generally. This area of 
 research has developed in an ultra-operationalist manner^ of late, 
 workers have seemed content to regard "empati^" as "what empathy 
 tests measure". The principal research activity has been corre- 
 lating "empathy", so defined, with other variables, ;/e shall 
 show, however, that the operation involves many unsuspected 
 sources of variation, so that scores are impure and results un- 
 interpretable. Studies based on myopic operationism are largely 
 waste effort, x^rhen the operation does not correspond to potentially 
 meaningful constructs. Defining a measure operationally is only 
 a first stage, preliminary to analytic studies which can refine the 
 measure and bring it closer to the intended construct. It ms^ be 
 of interest to note that this paper relies alir.ost entirely on al- 
 gebraic analysis as its research method. Even though analysis is 
 based on a model rather than actual behavior, it generates severrl 
 psychological hypotheses. 
 
Although our report deals with a specialized area of per- 
 ceptual research, it shares much of the perspective of Postman* s 
 important general revievj of perception (21;), His remarks are 
 peculiarly pertinent to studies of social perception, even thougji 
 he was referring especially to the "New Look" studies of perception 
 of words and objects: 
 
 "At this juncture of debate, we shall do xiell to pull up 
 short a moment and reconsider the fundamental operations of our 
 perceptual experiments, particularly as they bear on the validity 
 of the theoretical constructs linking perception to motivation and 
 personality*.. Experiments have shared a common tendency vdiich may 
 
 be called the projective bias a selective emphasis on central 
 
 motivational determinants at the expense of adequate attention to 
 the verbal and motor response dispositions of the subject and the 
 relation of these dispositions to the dimensions of the stimulus.,.. 
 We must, then reaffirm the critical importance of a full and precise 
 analysis of the responses as well as the stimuli which furnish the 
 basic data of perceptual experiments," 
 
 A Mathematical Resolution of Social 
 Perception Scores into Components 
 Model and notation 
 When data are gathered by means of a test consisting of 
 items (i = a, b, c, ••• k), these items define a k-dimensional 
 space, and the responses of any person define a point in that 
 space (10), One point is defined by O's actual responses, and 
 another ty J*s prediction for 0, x^^ denotes the response given 
 by Other (O) when describing himself on item i, VJhen a judge (j) 
 predicts what will say, x-ie have a prediction, Jq^a . x is 
 alw^s used to indicate self -descriptions, y to indicate pre- 
 dictions. 
 
 Error in prediction is represented hj the discrepancy between 
 Xqj|_ and yoiT* I^ some studies J*s accuracy is measiored by summing 
 
ly .. - X .1. \/e shall instead measure accuracy by the distance 
 
 from the predicted to the actual location in k-space, as determined 
 
 by the sum over items of squared differences. This formula is easier 
 
 to treat mathematically than the sum of absolute differences^ and 
 
 will ordinarily give about the same results, tJhen all items are of 
 
 a Yes - No form, so that the error on any prediction can only be 
 
 1 or 0, the two formulas give identical results. Our measure has 
 
 the important property of being invariant under orthogonal rotation 
 
 of axes (10), 
 
 We define the Accuracy with vjhich J perceives ty 
 
 ACC^ . = 2 (y . . - X . )^ (1) 
 
 oo 1 033 01 ' 
 
 Either ACC or ACC msy be used as a measure. Because it is much 
 easier to analyze ACC^ than ACC, we treat ACC^ throughout this paper. 
 This should be reiaembered in applying results. 
 
 Now we define 
 
 Grand mean of aH 
 self -description re- 
 sponses; average ele- 
 vation) • 
 
 Mean of all persons 
 on any item 
 
 Mean of argr person on 
 all items; his elevation 
 
 We define y 9 7 - and y correspondingly. Then x . - x 
 ••3 '^J o,j .1 
 
 would be the mean score on an item, figured as a deviation from the 
 
 overall mean, x - x is a score on the item, figured as a devia- 
 01 .1 
 
 tion from the item mean. We shall let x' . stand for x - x 
 
 01 oi o, 
 
 X . + X , and define y' . similarly. These are scores figured 
 ,3. • • 03.2.'' 
 
 as deviations from both nt^m and pp^rson mean, i »<=;., "ceiitRi-orl on 
 tests and persons," 
 
 X 
 
 • • 
 
 - - Z Z X 
 N k i oi 
 
 ^i' 
 
 N ""oi 
 
 X = 
 0, 
 
 izx 
 k i oi 
 
The Accuracy Score and Its Components 
 
 j^cciiracy in predicting one on one item may be measured 
 
 simply by the absolute value of the error. We have the folloTO.ng 
 
 identity: 
 
 ACC ..»i y..-x.l» j (y .-x ) 
 
 * iij^ . -y i ) - ( ^ - X )) 
 
 O.J ..J 0» *• 
 
 + (( y .. - y . ) - ( X -. X )) 
 •ij ..J •j-j •• 
 
 + ( y» .. - x» . )!! (2) 
 
 ^ ^ oij 01 ^' ^ ' 
 
 In the right member of (2), the first term compares the 
 elevation of J*s (the Judge's) predictions to the average elevation 
 of the criterion responses. Each person using a scale forms his 
 own frame of reference, and some people tend to use different 
 portions of the scale than others (?)• This first term -will be 
 small if J uses about the same part of the scale as the average 0, 
 
 The second term represents J's ability to predict the relative 
 elevation of the particular 0, It measures J<s knowledge of this 
 0*s mode of response on the scale. 
 
 The third term is, like the first, independent of the 
 particular 0, It represents J's error in estimating the 
 relative difficulty or popularity of the item. 
 
 The fourth component represents accuracy of prediction for 
 the individual Other, after we remove the first three differences. 
 It expresses J's knoxjledge of the shape and scatter of O's prof ale 
 of deviation scores. 
 
' '! ;> • 
 
 liJ .. 
 
6 
 
 Host research has been concerned with the accuracy of J as 
 a judge of all Others, This coiiLd be represented by an average of 
 his accuracy scores (ACC . ) with particular Others, 
 
 ACC^ = i 2 ACC^ = k ( f - 2r )^ 
 J N o oj ..j •• 
 
 * r2 (( y . -y . ) - ( X -x ))^ 
 
 N O.J ..J o» •• 
 
 ■*-2 (( y - J ) - (X -x ))^ 
 
 1 • ij • • 3 • 1 • • 
 
 + i 2 2 (( yt -x« )f (3) 
 
 N o i oij oi 
 
 These four terms are attributable respectively to the dif- 
 ference of J's elevation from the average, his errors in predict- 
 ing individual deviations in elevation, errors in predicting item 
 means, and errors in predicting individual deviations from the 
 item mean (after correction for elevation). We shall refer to 
 these as the Elevation (E) component , the Differential Elevation (DE) 
 component , the Stereotype Accuracy (SA) component , and the Differ- 
 ential Accuracy (DA) component . The last three of these require 
 separate discussion. 
 
 Differential Elevation QJE) 
 
 The Differential Elevation component measiires J's errors 
 in judging the "elevation" of O's responses. In some tests ele- 
 vation reflects insignificant response sets, and we should ignore 
 this component (cf. 10, p, k^3)» In other tests this component 
 reflects J»s judgment of the overall "desirability" of each 0, and 
 if so, it may be very important. 
 
The Differential Elevation component may be broken down by- 
 using the formula for sioms of squares of correlated differences: 
 
 2 2 2 
 
 DE = k ( a- + a- - 2o- a- r- • ) {k) 
 
 •^O.J O. O.J o« o. -^cj 
 
 2 
 The variance o- measures J*s tendency to predict that Others 
 
 differ in elevation. It represents Assumed Dispersion in Ele- 
 vation, later seen to be a component of "Assumed Similarity". 
 
 2 
 
 o- is the true dispersion in elevation. The correlation r- 
 
 ^o. ^o* ^o.j 
 
 (to be symbolized DEr) represents J»s ability to judge which 0*s 
 
 rate highest on the elevation scale. If every item measures morale, 
 
 for instance, the correlation shows how well J can judge which O's 
 
 say they have the highest morale. 
 
 Stereotype Accuracy (SA) 
 
 As used here "stereotype accuracy" refers to the person^s 
 ability to predict the norm for Others, It might be called 
 "accuracy in predicting the generalized other" (3). This score 
 depends on J*s knovjledge of the relative frequency or popularity 
 of the possible responses. In contrast to our stereotype inferred 
 from responses on many items. Gage measures an explicit stereotype. 
 He asks J»s to predict the model response among Others of a 
 specified type (l6, p, 8-11), Similar stereotype predictions are 
 obtained in studies of ability to estimate group opinion (e.g., 
 6, 18, 29). Evidence comparing these two types of stereotype, 
 would be valuable. 
 
 We may write; 
 
 SA^ = k ( a^ + oS - 2 a- a- r- - ) {$) 
 
 a y.. y. ^ n^ ± ^ ±-\^ ± 
 
 Here each variance is computed over items. The variance a« 
 
8 
 
 2 
 
 expresses hcu much J expects item means to vary, o- is the 
 
 X 
 
 2 'O 
 
 scatter of the actual means, SA represents ability to judge the 
 
 shape and scatter of the profile of item means, r- - (Stereotype 
 
 y.ij ^.i 
 
 Correlation, SAr) represents accuracy in judging mean profile shape 
 
 without regard to errors in judging profile scatter (i,e,, spread 
 in difficulty). 
 Differential Accuracy (DA) 
 
 Differential Accuracy measures ability to predict differences 
 between persons on any item. This component is a sum over items. 
 The component for ar^ item brealcs down: 
 
 (6) 
 
 2 2 2 
 
 DA = a + a • 
 
 -2a a r 
 
 ij y' x» 
 
 y' xi y» X 
 
 oij oi 
 
 oij oi oij oi 
 
 2 2 
 
 DA , summed over items, yields DA • Each variance in the formula 
 
 ij J 
 
 is taken over Others, a , is the Assumed Dispersion on the item 
 
 (see below). It resembles closely Gage^s concept of "rigidity" or 
 
 "adherence to stereotype" in prediction (l5, p. l6j 17 )• 
 
 The correlation (DAr) in (6) is a measure of ability to judge 
 which Others have highest scores on the item, when the score is 
 taken as a deviation from the Others* mean. There is one such 
 correlation for each item. 
 
 Implications 
 
 Seven aspects of J*s performance have been separated: 
 
 1, Elevation component: difference of predicted 
 
 average response from actual average 
 
 2, Assumed Dispersion in Elevation "\ 
 
 } Differential 
 
 3, Elevation Correlation DEr j Elevation 
 
 U* Predicted variation in item means ~; 
 
 I Stereo bypf» Ar.p.nrany 
 5o stereotype Correlation SAr 
 
6« Assumed Dispersion on ar^r item ' 
 
 (elevation held constant) [ Differential 
 
 \ Accuracy 
 7» Differential Correlation DAr j 
 
 The fact that the components are mathematically distinct does not 
 imply that they are necessarily uncorrelated. 
 
 Change in argr of these msy alter the Accuracy score. Surely 
 these aspects of social perception do not all reflect the same 
 trait, A person who uses the same region of the response scale 
 as other persons (Elevation is low) need not have superior insight. 
 ^-^And while judging which items have the highest mean seems to 
 require acquaintance with the norms of the group, a person might 
 possess such knowledge to a very high degree and yet lack diagnos- 
 tic skill which would permit him to differentiate accurately 
 betxieen individuals. At best, f ailiire to recognize the presence 
 of distinct components makes interpretation ambiguous, Chowdiy 
 and Newcomb (6) requested group members to predict what percen- 
 tage of their group would agree with each of many attitude state- 
 ments. Ability to make this prediction was judged by a difference 
 score, and this score correlated significantly with le adership 
 status. This score, however, combines our Elevation and Stereo- 
 type Jlccuracy components. We cannot conclude that their leaders 
 are better able to judge the specific attitudes in the group. 
 Until the components are separately measured we cannot rule out 
 the possibility that leaders simply used the correct range of the 
 scale more often than non-leaders. This, in turn, might reflect 
 willingness (or unwillingness) to use extreme percentages rather 
 than arQT more subtle perceptiveness. That such effects do occur 
 is shoTrin in a stucfy \sj Lorge and Diamond, who required juiiges t^ 
 
 y^ 
 
artj* r. 
 
10 
 
 estimate what propoition of 0*s would pass ability test items. They 
 found that poor judges were greatly helped simply by being told the 
 difficulty of a few items, "apparently the difference between 'poorest ' , 
 'mediocre', and 'best' judges is that the 'best* judges have some experi- 
 ential reference for the per cent of the population that can pass an item. 
 Giving such referents to the 'poorest' and 'mediocre* judges,., leads to a 
 
 significant reorientation of such judgments," (20,p,33) When judges re- 
 sponded only to the items, the best judges had a mean Stereotype Correla- 
 tion of ,73, and the poorest one of ,56, /.fter information indicating an 
 appropriate reference level was given, the same groups had mean correla- 
 tions of ,77 and ,73« The difficulty encoiintered in inteipneling the Chow- 
 dry-Newcomb study does not arise in two recent treatments of the saniO 
 problem (18,29) where subjects are asked to predict what ranks will be 
 assigned to certain stimuli. The ranking method eliminates elevation and 
 dispersion differences from the responses, and therefore confines scores to 
 the Stereotype Correlation, An Alternative and more informative method 
 
 might be to analyze data of the Chowdry-Newcomb type in terms of the sepa- 
 rate components so as to determine how leaders behave on each. At worst, 
 failure to identify the ccmpcnents of the Accuracy score leads to arti- 
 f actual correlations. Only a few of the mar^ examples in the literature 
 need be cited, Norman and Ainsworth (CO) report a large number of corre- 
 
 latioiis between Accuracy ("Empathy") and Assumed Similarity ("Projection"), 
 Since +he accuracy score contains assumed similarity components, there 
 would necessarily be an overlap between Those two scores even in a situa- 
 tion where both sets of responses are determined strictly by chance. The 
 correlations have no psychological meaning. In Esrmond's study (12) it was 
 reported that persons with high Accuracy are also most easily judged. But 
 a person who uses the scale in a typical manner will have a lo;>r Elevation 
 Component; and thus will have lower Elevation erros in judging him simply 
 because of this typicality. This would happen even if the other predicted 
 his responses mth^ut ever meeting himi Perhaps social psychologists 
 should take what comfort they can from Bertrand Russell's remark that 
 physicists "have not yet reached the point where they can distinguish 
 between facts about relativity and mathematical operations which may 
 have nothing to do thereTiith", 
 
n 
 
 Analysis of Assumed Similarity Score into Components 
 Assumed Similarity (AS) may be determined (see lU, for 
 example) for a single Other by the formiila: 
 
 AS^ = I ( y - X f (7) 
 
 JO ^ oij ji 
 
 y is the perception of by J, and x is J*s statement about 
 oij ji 
 
 himself, (Sometimes "ASo", Assumed Similarity between two Others 
 
 selected in a certain manner, is computed). Some investigators 
 
 have measured AS over many Others, to get a general score called 
 
 "projection" or "identification" (22), 
 
 IJe msy brealc AS into components as we did ACC, If, as before, 
 
 we measure AS by a distance formula based on sums of squares, 
 
 AS^ « i Z ? AS^ = k ( y . - X . )2 
 J N o 1 oij ..J .0 
 
 2 
 
 + k , a- 
 
 :-^ . . . a 
 
 t-2 (( y -y ) - ( X -X )) 
 1 -IJ ••J IJ "J 
 
 + 2 o2 
 
 i y 
 
 (8) 
 
 oio 
 Assumed Similarity, therefore, contains four components. Equation 
 
 (8) is simpler than the corresponding formula for ACC because 
 
 some terms vanish, 
 
 Assui-ncd Elevation (AE) 
 
 The first term we may call the Assumed Elevation (AE) 
 
 component. It measures J«s tendency to assvime that Others have 
 
 the same average response as he does. This component is important 
 
 if items are polarized so that a high score on each represents 
 
 good adjustment or some other interpretable qualityj the score 
 
 then shows whether J regards the average as similar to himself 
 
 in this central dimension, tiBR^ 
 
12 
 
 Assumed Dispersions (APE, API), 
 
 The second component is the Assumed Dispersion among Others in 
 elevation. The fourth is the Assumed Dispersion on specific items 
 after differences in elevation are removed. These dispersions have 
 already been encountered in equations (U) and (6) as components of ACC« 
 ¥e shall refer to them as Assumed Dispersion in Elevation (ADE) and 
 Assumed Dispersion within Items (API), respectively. 
 Assumed Self-Tsrpicality (AST) > 
 
 The third component measures the discrepancy between J*s percep- 
 tion of the average and his self-description. This component tells 
 whether J regards his own profile as typical in shape. Or, we might say, 
 this component shows the similarity of J's self -perception to his implicit 
 stereotype of Others (elevation held constant). We follow Gage in calling 
 this Assumed SeUT-Typicality (AST) (l6, p,17). 
 
 Of the four components, only AST divides into separate variance and 
 correlational terms, 
 
 AST = k ( a? / a^ - 2 a a- r - ) ^^^ 
 y .^ X. . X. . y . . X .y . . 
 .ij 10 10 .13 iO .10 
 
 The variance among the y's represents the tendency of the predictor to 
 predict different means for different items. The correlation represents 
 the similarity between his self -description and the average profile, after 
 removing differences in elevation and scatter from consideration. We may 
 call it the Self -Typicality Correlation (STr ), 
 
 To summarize: the components of AS are of tvio types, ADE and ADI 
 involve Assumed Similarity between Others ; i.e., a tendency to differ- 
 entiate, iJ?S and /ST represent Assumed Similarity of self to average 
 Other , These types seem logically distinct, but a subsequent section 
 will indicate the probable desirability of combining AE with ADE, and 
 AST with ADI. 
 
■4= 
 
13 
 
 Optimizing Predictive Decisions 
 
 Insofar as our mathematical model is an acceptable approxi- 
 mation to real conditions, we can reason mathematically to de- 
 termine how a person may improve his judgments. We have assumed 
 
 that the goodness of predictions can be evaluated b^ the mean 
 
 2 
 
 square error. Taking the derivative of each component of ACC , 
 
 and setting that derivative equal to zero, we find that ACC be- 
 comes smaller, and therefore prediction improves, xirhen 
 
 (a) J has a typical response set, 
 
 (b) c- approaches r- - a- , Here the variance is 
 
 y.io \±y.±3 \± 
 
 over items. This means that a- should not exceed 
 
 y ii 
 a- y and should be near zero if the Stereotype 
 
 Correlation is low. If this correlation is low, 
 
 .he 
 the more/differentiates among items, the poorer 
 
 is his accuracy. 
 
 (c) o approaches ^^, , ^x« ' * ^^® variance being over 
 
 Others, This means that o ^ should not exceed a , , and 
 
 y X 
 
 shoiild be near zero if the Differential Correlation is 
 low. This principle is true for accuracy of prediction 
 on any single item, and for the elevation score. 
 It has not been possible to determine the conditions which maxi- 
 mize accuracy as measured by other formulas (such as the mean of 
 ACC .)but a result of the same general character would be 
 expected. 
 
 Effec ts of differentiation on prac tical decisions 
 
 These formal principles indicate that there is an optimal 
 degree of differentiation in maldng judgments, jf a Jiidge cnn 
 
11; 
 
 make accurate judgments as to the relative location of Others 
 on a continuum, then he is vdse to make a as large as a — 
 never larger. But if he is forced to base his judgment on 
 inadequate cues or if the available personality theoiy and situ- 
 ational knowledge do not permit trustt\rorthy inference, then he 
 should treat people as if they were very nearly alike. The 
 person who attempts to differentiate individuals on inadequate 
 data introduces error even when the inferences have validity 
 greater than chance . This is consistent with Gage»s evidence 
 that judges predict a stranger more correctly when they describe 
 the typical person of his group than when they try to describe 
 him as an individual (l6, p.lO), 
 
 Tlie variation of J's predictions indicates how much he 
 differentiates. For example, a teacher estimating IQ's in a 
 class might spread them from 90 to 110, or from 70 to 130. We 
 would expect the judge who perceives greater differences to 
 spply viore sharply differentiated treatments to the persons. 
 His a is essentially a weighting, or an expression of his con- 
 fidence in his own discriminations (cf, 19, p,201), A person 
 who knows that the expected s,d, for IQ»s is l6 might try to 
 predict so that his estimates would have this s,d, but unless 
 he is a perfect judge, this is unwise. He will have less error 
 if his predicted s.d, is less than l6— how much less depending 
 on the correlational accuracy of his predictions. 
 
 If two diagnosticians can each judge some trait with corre- 
 lational validity ,i;0, the one xfho differentiates strongly (i,e*, 
 makes extreme statements) will mslce far more serious absolute 
 errors than -on© who diffeientj.ates moderately. Indeed, the 
 
as'rtoi, e- 
 
 r.njjp- 
 
15 
 
 person "who makes extreme differentiations on the basis of a 
 validity of ♦1^0 msgr make worse predictions, judged by absolute 
 magnitude of errors, than a judge who has zero correlational 
 validity but makes no false differentiations. 
 
 "Every pupil has his oX'jn pattern of readiness, and the 
 teacher must fit methods to that pattern, not treat the pupil 
 in terns of the statistical average" (9, p«73)« Statements such 
 as these, commonly made in teacher-training, now appear to require 
 qualification. From our evidence the degree of adaptation desirable 
 depends on the adequacy of the diagnostic information. If the 
 teacher is not well informed regarding the unique patterns of his 
 pupils, he should probably treat them by a standard pattern of 
 instruction xjhich has been carefully fitted to the typical pupil. 
 Modifying his plans drastically on the basis of limited diagnostic 
 information may do harm, A similar argument applies to clinical 
 diagnosis and industrial leadership. Differentiation is harmfiil 
 if the extent of adaption or differentiation exceeds the amount 
 justified by our accuracy in differentiating. This 
 is a distinct reversal of the view that judgment is always improved 
 by taldjig into account additional information which has validity 
 greater than zero. Investigators have noted a "central tendency 
 of judgment", vrhich leads to lower dispersion among estimates than 
 among objects, Ii/hereas formerly "the central tendency of judgment" 
 was regarded as a source of inaccuracy (1, p,521) our analysis 
 shows that this tendency may have beneficial consequences* 
 
 Teachers may properly modify treatments considerably to fit 
 individual differences provided they are well able to ju^dge. those 
 
16 
 
 differences* They might be expected to judge differences in 
 past achievement in arithmetic qiiite accurately j if so, they 
 could profitably provide quite different treatments (e.g., dif- 
 ferent assignments) for different individuals. But if it is 
 hard to judge some other quality (e,g», creative potential in 
 art), then it is a great mist alee to differentiate treatment. 
 Treatment from individuals should depart substantially from 
 that suited to the average of the group only when dependable 
 information is available to guide the adaptation. 
 Illustrative Analysis of Cornell Data 
 
 To illustrate our system of analysis, we use data kindly 
 provided by Bronf enbrenner and Dempsey. The data were gathered 
 at Cornell University primarily for the purpose of pilot analyses 
 such as ours. Only eight subjects and nineteen items are involved; 
 and we actually employ only eight of the items. 
 
 In the Cornell experiment (U)^ the eight subjects vrere can- 
 didates for employment as interviewers. Each person interviewed 
 each of the seven others. In each interview, each man was to 
 obtain information about his partner. Following the interview, 
 each person filled out a form stating his o^^m reaction and predicting 
 what his partner would say. There are eight items, each to be 
 judged on a four-point scale. One item is: 
 
 To what extent did you feel at ease during the interview? 
 
 a, very much b. a good bit c, only slightly d, not at all 
 
 The respective responses are scored 1-2-3-U, Completion of the 
 design provides seven self -descilpti ons and. Gcvon prerli rit.i ons by 
 each man (also, seven for each man). 
 
17 
 
 We have taken two simplifying steps which might be 
 illegitimate for purposes other than demonstration. In every 
 instance, we have used the average of 0*s responses over all 
 seven interviews as his true response, x . • This discards in- 
 formation on 0*s variation from interview to interview* Secondly, 
 we treat J*s self-description as a "prediction of himself". This 
 "prediction" is taken as perfectly accurate. By this device, we 
 deal at all times with eight Judges and eight Others, and the 
 criterion is made the same for every person. 
 
 Accuracy Scores for Eiglit Persons 
 
 Table 1 presents the ACC score for each person, and his score 
 on each component. Table 2 organizes the same data to shox/ the 
 person* s relative position in the group. Based as they are on 
 only eight cases and eight items, these data and subsequent 
 numerical results are illustrative, and not a proper basis for 
 generalization. They m^ be useful to guide future studies. 
 Magnitude of components 
 
 The Differential i\ccuracy Component has substantially larger 
 variance than the others, and therefore has much greater influence 
 on ACC, Although Elevation has a smaller mean than Differential 
 Elevation and Stereotype Accuracy, the variancesfor these three 
 scores are nearly equal; they pl^ an equal part in determining 
 individual differences in Accuracy. The correlations DEr and DAr 
 are generally low but positive. The Stereotype Accuracy correla^ 
 tions, however, averaged ,7l4. 
 Relatior. of differentiation to accuracy 
 
 The data illustrate our mathematical principle that any 
 accuracy component is made smaller as the predicted standard 
 
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 deviation (a ) approaches the product of the related correlation 
 with the actual standard deviation {oy^m Consider, for example, 
 the results on Stereotype Accuracy and its constitu nts. Person 8 
 is an excellent Judge, according to his stereotype correlation of 
 «92« But he expects too much variation in the item means (.76 
 compared to an actual s,d« of .lili). As a consequence #8 has a 
 poor Stereotype Accuracy score despite his excellent ability to 
 discriminate between items. The best Stereotjrpe Accuracy scores 
 are earned by #1 and #5^ who have high correlations and who pre- 
 dicted variance close to the actual variance. Another comparison 
 worth noting involves the Differential Elevation and Differential 
 Elevation Correlation of #3 and //8, These persons have the same 
 Differential Elevation Correlation, but #3 underestimates the 
 variation in elevation, and #8 overestimates it. As expected, 
 #3 earns the better Differential Elevation score. 
 Reliabilities and Intercorrelations 
 Reliabilities of components have been determined, where 
 practicable, ly the Alpha formula (7), Reliability of Accuracy 
 over items was ,llt. This value indicates the expected consis- 
 tency of scores if Accuracy for each predictor were estimated 
 using tvxo independent sets of items. This reliability over 
 Others was ,37* This is the consistency expected if Differential 
 Elevation scores were estimated from two independent sets of 
 Others, The results on Differential Components throw further light 
 on this resiilt, 
 
 LtiLfierential icciaracy was strikingly reliable over items: L «= 
 ,73» That is to say, some predictors were consistently good 
 
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21 
 
 over all items, others consistently poor« But when we exairdne 
 the coinponents of DA, we find that 
 
 (a) c is reliable over items ( cC « .79) 
 
 (b) DAr, the measure of accuracy in locating 
 
 others, is not ( jjC = ,18) 
 In this sample Differential Accurajcy shows reliability because 
 some persons have consistently low assumed similarity* This 
 makes them consistently inaccurate predictors because Differ- 
 ential Accuracy Correlation is generally low). No adequate 
 estimate could be obtained for the reliability of Elevgtion, of 
 Stereotype Accuracy, or of Accuracy over Others. We examined 
 wl^ Accuracy has reliability much lower than Differential 
 Accuracy, one of its components. Apparently this occurs be- 
 cause the sign of the stereotype error has a substantial effect 
 on accuracy on any one item and therefore lowers the correlation 
 from item to item. 
 
 Our limited data suggest the accuracy components tend to 
 be unreliable except where reliable differences in assumed 
 similarity affect the component. Stone and Leavitt (28) report 
 very low consistency (-.0? to 30) of accxiracy scores in pre- 
 dicting different children on a fairly long test, but a median 
 consistent of .63 between two halves of the test for the same 
 child. They then trace the latter consistency to consistent 
 favorable sets toward a given child, and to assumed similarity. 
 Further work is needed to establish which independent components 
 of Accuracy can be reliably measured. 
 
 In Table 2 we note that Number 1 is consistently superior 
 
22 
 
 on various components of accuracy and ^k is consistently 
 inferior. But #7, the best predictor as judged 1:^ Differential 
 Accuracy Corfelxticnls the poorest on Differential Elevation 
 CbrrcsLstion and next-to-poorest on Stereotype Aceuracy- Ccmpcnent* 
 Table 3 presents the intercorrelations of the eight measures of 
 accuracy. M asterisk indicates pairs of variates which are 
 experimentally linked; these correlations are higher than would 
 be expected from independent measurements. Being based on only 
 eight cases, the correlations cannot be interpreted confidently. 
 The correlations are low but many of them are as high as the 
 accorrpanying reliabilitfes. 
 
 Only one firm recommendation can presently be made. 
 Future studies of predictive accuracy should measure the com- 
 ponents separately, preferably using two independent sets of items 
 and Others, Such measurement mXL permit accurate determina- 
 tion of reliabilities, of the relation between the components, 
 and of their relation, if ar^r, to external criteria. Only after 
 such research can we decide how many important components are 
 present i^thin the overall Accuracy score presently used in most 
 research on social perception and which unwanted components must 
 be suppcreeBedby appropriate design of tests and scoring keys. 
 
 Illustrative Analysis of Assumed Similarity Scores 
 In Table I;, the Assumed Similarity scores of the eight 
 Judges are divided into components. Table $ presents the same 
 information in rank form, and Table 6 presents the intercorrela?- 
 tion. The relatively large variance of ADI, Assumed Dispersion 
 on each item, indicates that it bs© great influence on individual 
 
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 differences in overall Assumed Similarity, 
 
 The correlations show great overlap of Assumed Elevation 
 Xidth Assumed Dispersion in Elevation, and Assumed Self -Typicality 
 with Assumed Dispersion \d.thin Items, The tendency to differen- 
 tiate emcng Others is accompanied by a tendency to differentiate 
 the average Other from oneself. This result is partly an arti- 
 fact, resulting from using each person's self-description as one 
 of his "predictions". Even allowing for this, our correlation 
 suggests treating only two components of AS: Assumed Similarity 
 in Elevation (AE + ADE) and Assumed Similarity in Pattern (AST + 
 ADI), The correlation between these two variables is negligible 
 ( X> = •21), Further evidence is required to determine how to 
 divide Assumed Similarity snd which components merit serious 
 investigation. 
 Correlation of Assumed Similarity with Accuracy 
 
 Table 7 gives the correlations of Assumed Similarity 
 components with Accuracy components. 
 
 The Judge «s "Implicit Personality Theory" 
 
 We turn now to an aspect of social perception data which 
 msy prove to be particularly significant, VJhen a Judge makes 
 predictions for a large number of Others, these predictions 
 define a corresponding distribution of points in the variate 
 space. This distribution mary be regarded as a description of 
 the generalized Other, representing the Judge's view of both 
 central tendency and individual differences. The Judge's 
 generalized perception may be an insert ant indicator of his 
 expectations regarding others, lie shall discuss the general 
 
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21 
 
 significance of this perceptual system before tracing its 
 effect on social perception scores. 
 
 J's distribution is to be examined in terms of the means 
 and variances on the original variates, and also in terms of the 
 covariances. The mean msgr be regarded as J's stereotj^e; if the 
 mean Other in his descriptions is "hostile", for example, this 
 may be highly significant. The variance indicates J*s tendency 
 to differentiate along a dimension; it is represented in Assumed 
 Dispersion within Items, The covariance is interpreted as indica- 
 ting the relations J expects to find among variates, A given 
 Judge may cijstomarily report the same persons as high on both 
 "quietness" and "shyness", for instance; or on both "ambition" 
 and " selfishness" ♦ These aspects of the distribution reveal 
 J»s view of others and the connotation of personality traits 
 for him. 
 
 We suggest that these means, variances, and covariances 
 describe J«s implicit theory of personality. The expectations 
 J has of others constitute his view of personality end presumably 
 direct his responses to Others, IJhile the mean of J*s predictions 
 might be consciously controlled to give some impression J regards 
 as desirable, it is quite unlikely that J is aware of the corre- 
 lation among his responses. For this reason, we believe that the 
 distribution represents J»s implicit theory of personality. 
 Certainly it represents associations and norms of which J is not 
 necessarily aware. 
 
 Osgood has drawn attention to the possibility of studying 
 the semantic equivalence of stimuli by testing whethex- they are 
 
(?..!'. ■ 
 
28 
 
 • 
 
 used sinilarly (21), Omt method is quite similar, determining 
 as it does what personality traits J regards as occurring 
 together, i/e propose to examine the reference frame witliin which 
 J locates particular others. 
 
 This concept can be illustrated "by using a small portion of 
 the Bronfenbrenner-Dempsey data. Their test required J to pre- 
 dict responses of eight persons (including himself) on these 
 questions : 
 
 1» In general, how openly did you eiq)ress 
 
 your feelings and emotions during the interview? 
 
 2. How much interest did you feel in the other 
 lofln as a person? 
 
 3« Hoxj much were you aware of how he was feeling? 
 
 k» How much opportimity did you give him to 
 interview you? 
 
 5« How much important information were you able 
 to get about him? 
 
 6« To what extent did you feel at ease during 
 the interview? 
 
 7. To what extend did you succeed in establishing 
 a good interviewing relationship? 
 
 8, To what extent did you feel like the person 
 being intervievjed rather than the person doing 
 the interviewing? 
 
 A four-point response scale was used for these items, a low 
 score indicating a positive answer. 
 
 For Judge 3> a poor predictor, we determined the mean, 
 variance, and covariance of his predictions. The matrix of co- 
 variances was factored by a pivotal method akin to square-root 
 factor analysis (31)> intended to yield interpretable factors. 
 Table 8 shoxjs the loadings on three factors with item means and 
 
■'.'ij. ■".:?■- 
 
29 
 
 variances. 
 
 The means for Judge 3 show no striking features, especially 
 xrhen considered in relation to the true means presented below. 
 The variances indicate that 7^3 regards others as f airily uniform 
 in their awareness of him (item 3)^ and as varying especially in 
 their openness, ease, and feeling of dominance (items 1,6,8), 
 The first two dimensions of #3*s perceptual space crc plotted as 
 Figure 1, Little confidence can be placed in factors based on 
 eight cases, but we would otherwise interpret Factor I as repre- 
 senting a feeling of being under pressure. It is notable that 
 #3 regards those persons who are most open (item 1) as being 
 least at ease (item 6). Factor II shows a link between items 
 U and 5^ getting and giving information. Factor III is indis- 
 tinct. It is notable that items 6 and 7 are correlated; a "good 
 interviewing relation" is perceived by #3 as one wheire the inter- 
 viewer is at ease J Such a finding regarding #3's perception, if 
 better substantiated, might have much diagnostic importance. 
 
 The literature contains many studies of correlation betx-jeen 
 ratings which bear on the perceiver*s frame of reference. The 
 studies of halo effect in rating suggest the existence of a strong 
 general good-bad factor. These studies have not examined raters 
 separately, Frenkel-Brunswik reports that ethnocentric individuals 
 see others in black-and-white terms, the "good", "strong" traits 
 going together (l5> See also 25), She does not present correla- 
 tional data, but she is essentislly stating that halo effects 
 are stronger in such raters. In our language, their covariance 
 matrix is loaded with one factor, vrliile non-authoritarians use 
 
'li'.'i-i.uj 
 
30 
 
 maDy factors and do jiot emphasize the general evaluative dimension, 
 Steiner (2?) has substantiated this conclusion, and discusses his 
 results in terms of the perceiver's "trait contingencies", that 
 is, in terms of the perceiver's frame of reference. "The indi- 
 vidual's assumption that certain attributes belong together is 
 expected to influence his percept of the person with xirhom he is 
 interacting" (p.3l49). Steiner 's data are restricted to group 
 differences, but his theory is not. Our position differs slightly 
 from steiner 's in that we emphasize the implicit contingencies 
 of which the perceiver may be quite unaware, Steiner 's method, 
 in its present form, requires the psrcefcier to say explicitly what 
 contingencies he expects. 
 
 Two other studies show differences in the perceptual ref- 
 erence frame of groups, Wickman's well known stucfy (30) showed 
 that teachers expected different traits to correlate with mental 
 health than did mental hygienists, Moore (21) performed a 
 factor analysis of ratings given non-commissioned officers by 
 their subordinates, and also of ratings given by their superiors. 
 The factor patterns differed. For instance, superiors coupled 
 "leadership" with eagenness and responsibility, but the sub- 
 ordinates viewed "leadership" as closely linked with intelligence 
 and skill. 
 
 None of these studies of groups examines the perceptual 
 space by which an individual describes personality, but the 
 evidence supports the belief that important individual differ- 
 ences exist. In view of our interpretation of the perceptual 
 distribution as an implicit personali+y theory, special interest 
 
at-, V 
 
31 
 
 would attach to studies or ratings given by clinical psychologists 
 or psychiatrists of different schools, or having different amounts 
 of training. One objective of instruction in the field of person- 
 ality is to modify oversimple views students may hold. If our 
 procedure does reveal covert and unconscious conceptions, it may 
 be a usef-ul device for evaluation. 
 
 Effect on accuracy scores . The Judge's distribution of 
 Others has been interpreted here as a standing system of meanings 
 which delimts the space within which he locates Others, It is 
 obvious that any such delimitation would affect social perception 
 scores. 
 
 Discrepancies between perceived mean and actual mean lower 
 Stereotype Accuracy, 1/e have shown earlier that Accuracy declines 
 if Assumed Dispersion departs from an optimal value. The corre- • 
 lational effects are a bit less easy to perceive. 
 
 Correlations describe the shape of the distribution of 
 Others, If traits 1 and 2 are uncorrelated, then Others will 
 have a roughly circular bivariate distribution. If a Judge re- 
 gards 1 and 2 as correlated, attributing both to the same persons, 
 his perceived distribution will be elliptical. His perceived dis- 
 persion along the dimension 1+2 will be greater than in the true 
 responses, and his accuracy will suffer, ife can view the example 
 in another w^. Suppose the judge predicts variate 1 perfectly 
 but believes that variates 1 and 2 correlate 1,00 when they have 
 a true correlation of zero — - then he must have substantial 
 error in predicting variate 2, He can predict 2 accurately only 
 if he perceives the covariance of 1 xd-th 2 accurately. 
 
,;♦^.^^? 
 
32 
 
 Data reported by Crow (11, p.86) show this phenomenon 
 clearly. As part of a larger stuc^, he asked Judges to predict 
 what would be the first word missed by a patient on a vocabulary 
 test and what was the highest level attained (called tasks Dl 
 and D2.) The correlation of Judges' accuracy on Dl t.jith accuracy 
 on 12 was positive and significant for five of ten patients, but 
 negative and significant on two patients. Judges tended to expect 
 a correlation between the two scores, and when there was a true 
 correlation they did well; where it was negative, the Judges 
 could not be accurate on both predictions. There was a rank 
 correlation of ,97 (over patients) between consistency of accuracy 
 scores, and consistency of the patient's performance. 
 
 The Cornell data were examined to determine the covariance 
 between items in self-descriptions. The resulting "criterion" 
 matrix was factored, with the results shown in Table 9 and plotted 
 in Figure 2 (first two factors). This pattern is different from 
 that of #3 (Table 8) in several respects. Notably #3 overdifferen- 
 tiates on all items. The first factor for #3 lumps openness and 
 lack of receptivenessj these variables are divided among two 
 factors in the criterion. In the criterion, being at ease (item 6) 
 is positively related to openness. It is especially interesting 
 that "feeling like the person being interviewed" is, for the 
 group as a whole, positively correlated with being at ease; but 
 for #3 these items are negatively correlated. With a view of 
 people so discrepant from the facts, it is not surprising that 
 #3 has a low ACC score. 
 
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33 
 
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 Table 9 
 
 Factors in the Criterion Covariance Matrix 
 
 Determined by Pivotal Method 
 
 I II III 
 Openness Receptiveness Passivity h^ Variance 
 
 Mean 
 
 1 
 
 2.06 
 
 2 
 
 .lU 
 
 3 
 
 .09 
 
 k 
 
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 5 
 
 .22 
 
 6 
 
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 -.10 
 
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 " ko% 
 
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 ' 1 nnf 
 
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 -.l|0 
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 .55 
 
 .60 
 
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 3.60 
 
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 -.30 
 
 h.h9 
 
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 1.93 
 
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 .30 
 
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 1.07 
 
 1.80 
 
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 .5U 
 
 .67 
 
 2.05 
 
 1.16 
 
 1.93 
 
 l.It2 
 
 1.57 
 
 .09 
 
 .76 
 
 .91 
 
 1.6U 
 
 .85 
 
 .80 
 
 2.I45 
 
 3.09 
 
 2.U6 
 
 11.35 
 
 13. 3U 
 
 16.17 
 (siun) 
 
 18^^ 
 
 8552 
 
 
 
 85^ 
 
 Recoiranendations 
 Studies of perception may be concerned either with constant 
 processes or with variable processes. When social perception is regarded 
 (as in Allport 1, pp. U99-5U8) as a process of interpreting the expressive 
 cues Other presents, or of empathizing with him, the search is clearly for 
 a variable process. Yet we have seen that the measures currently used 
 are affected by both constant and variable processes, and therefore cannot 
 5^prve voll to investigate either. As Crow states (11, p.57)s 
 
35 
 
 "The difficulty stems from failure to recognize 
 that two meanings of predictive accuracy are 
 involved* The use of the correlation scoring 
 method (either r„ „ or r„, ^, ) defines 
 
 oiyoij ^ oiy oij 
 predictive accuracy as the ability to approxi- 
 mate the actual situation, Ey the difference 
 score method a subject is penalized for a sys- 
 tematic error in estimation of the magnitude of 
 the actual situation, j^- the correlation method 
 the subject is not so penalized. Conversely, a 
 subject is penalized by the correlation method 
 if, although he has approximated the actual 
 situation, his predictions do not vary concomit- 
 antly with the actual scores. Each of these 
 scoring methods has its advantages and disadvan- 
 tages. The choice of vjhich technique to use will 
 depend upon the empirical relationship between the 
 procedures," 
 
 An argument can be presented for concentrating attention on 
 constant processes, taking up variable processes only after the 
 constant processes are dependably measured. Constant processes 
 in the perceiver have potentially great importance because they 
 affect all his acts of perception* Individual differences in 
 constant processes need to be measured dependably so that their 
 influence can be discounted in studies of variable processes. 
 Moreover, identifying constant errors should permit training to 
 eliminate such biases; this may be the most effective way to 
 improve the social perception of leaders, teachers, and diagnos- 
 ticians. 
 
 Not all constant processes are of theoretical iinportance. 
 It may be that response sets in filling out a questionnaire, for 
 example, arise as much from the inflection of the experimenter 
 in reading the directions as from any personality characteristic 
 of the subject. It is particularly difficult to decide whether a 
 tendency to say "yes" on questionnaire items, or to give favorable 
 
36 
 
 answers to such questions, is a transient mental set or a reflec- 
 tion of one's personality (7,10), Some decision must be reached, 
 hoijever, in order to design measures which include or exclude 
 these sets as the investigator's theory dictates, 
 
 Ue may venture to suggest which components of social per- 
 ception measures deserve research attention, recognizing that 
 the ultimate importance of the components depends on whether 
 they relate to important criteria, 
 
 (1) To some extent, the Elevation Component reflects 
 ^^Jhether J interprets the words defining the scale in the same 
 manner as others do. It appears relatively unfruitful, therefore, 
 as a source of information on his perception of others. It should 
 be separately measured or eliminated from consideration. 
 
 This is consistent vrLth Postman's view: 
 "In experiments concerned with the determinants 
 of perceptual selectivity, the contribution of verbal and 
 motor response habits must be specifically evaluated and 
 wherever possible held constant. The effects of the in- 
 dependent variables can then be evaluated against an 
 empirical baseline defined by the response habits of 
 the subjects," 
 
 (2) The Assumed Similarity measures reflect a general 
 orientation toward others. Perhaps the tendency to differentiate 
 which these indices measure is a reaction shown only in the testing 
 situation. But the fact that significant behavioral correlates 
 have been found for Assumed Similarity by Cass, Fiedler, and others 
 (2,5,13,lii,25,26) suggests that this is a generalized mental set 
 influencing both test snd non-test behavior. Investigators xro\ild 
 
37 
 
 do well, hoxirever, to consider Postman's conclusion that response 
 dispositions can be established unambiguously only if they are 
 measured ty more than one "type of response. (2U, p»27) 
 
 Components related to /.ssumed Similarity include Assumed 
 Dispersion in Elevation, Assumed Dispersion over items, Assiuned 
 Elevation, and i?ss\jmed Self -Typicality. Further research is 
 required to determine whether these should be measured separately 
 or combined. 
 
 (3) Stereotype Accuracy expresses hovr closely J«s implicit 
 picture of the generalized Other agrees with reality. HLfferences 
 of this soi*t are probably important. Attention should be given 
 to the nature of J's errors, as well as to the overall magnitude 
 of the component, 
 
 (U) The Judge's Perceptual Space, studied as a whole, 
 includes not only information on his Stereotype and his Assumed 
 Dispersion, but also on the wsy in which he organizes the field 
 of personality. The only evidence now available on this type of 
 constant cognitive process is sketchy, but it suggests strongly 
 that this is a most important area for research, 
 
 (5) The Elevation Correlation and the Differential Accuracy- 
 Correlation are measures of J's senstivity to individual differ- 
 ences. It is these measures which will reflect his ability to 
 interpret expressive behavior, or his ability in differential 
 diagnosis. Present evidence on the reliability of measures of 
 this character is not encouraging, and it msy be that study of 
 constant processes in social perception will prove more 
 profitable. But those x^ho wish to study "empathy" or "social 
 sensitivity" as it has usually been conceptualized should reduce 
 
38 
 
 their measures to these correlational components. Stereotype 
 components should he eliminated (10, p,U58)« 
 
 Social perception research has been dominated by simple, 
 operationally defined measures. Our analysis has shown that 
 any such measure may combine and theret^r conceal important 
 variables, or msgr depend heavily on unwanted components. Only 
 by careful subdivision of global measures can an investigator 
 hope to know what he is dealing with. Our analysis makes es- 
 pecially clear that the investigator of social perception must 
 develop more explicit theory regarding the constructs he intends 
 to study, so that he can i^educe his measures to the genuinely 
 relevant components. 
 

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References 
 
 !• Allport, G, ¥• Personality: a psychological interpret at ion « 
 New York, Holt, 1937. 
 
 2, Bieri, J, Changes in interpersonal perceptions following 
 social interaction, J, abnorm, soc. Psychol. , 1953 ^ 
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 3» Bronfenbrenner, U« and Harding, J, Components and 
 
 correlates of social sencjtivity, Ainer, Psychologist , 
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 k» Bronfenbrenner, U. and others. The analysis of social 
 sensitivity (empathy), Amer, Psychologjist, 1952, 7 
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 5, Cass, Loretta K, Parent-child relationships and delinq- 
 
 uency, J, abnorm, soc, Psychol, , 1952, U7, 101-10!|, 
 
 6, Chowdry, Kalma, and Newcomb, T, M, Ihe relative abilities 
 
 of leaders and ncn-leaders to estimate opinions of 
 their own groups, J, abnorm, soc. fsychol. , 1952, U7, 
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 7, Cronbach, L, J, Further evidence on response sets and test 
 
 design, Educ. psychol, Ileasmt , 1950, 10, 3-31, 
 
 8, Cronbach, L, J, Coefficient alpha and the internal structure 
 
 of tests, Psychometrika, 1951, l6, 297-33U. 
 
 9* Cronbach, L, J, Educational psychology . New York: Harcourt, 
 Brace, 195 U, 
 
 10. Cronbach, L, J., and Gleser, Goldine C, Assessing similarity 
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 11« Crow, W, J, A methodological study of social perceptiveness. 
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 12, Dymond, Rosalind F, Personality and empathy. J, consulting 
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 13* Fiedler, F. E. A method of objective quantification of 
 
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 15« Frenkel-Brunswik, Else. A study of prejudice in children. 
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l6» Gage, N. L» Judging intei'ests from expressive behavior. 
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 18, Greer, L, F,, Galanter, E. H., and Nordlie, P. G. Inter- 
 
 personal knowledge and individual and group effectivoriess, 
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 !?• Kelly, E, L, and Fiske, D. W, The prediction of performance 
 in clinical psychology . Ann Arbor: Univer, of Michigan 
 Press, 1951. 
 
 20, Lorge, I, and Diamond, Lorraine K, The value of information 
 
 to good and poor judges of item difficulty, Ed. Psychol, 
 Measmt., 195U, Ih^ 29-33. 
 
 21, Moore, J, V, Factor analytic comparisons of superior and 
 
 subordinate ratings of the same WCO supervis'-rs. HRRC 
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 22, Norman, R, D. and Ainsworth, Patricia. The relationships 
 
 among projection, empathy, reality, and adjustment, 
 operationally defined, J. consult. Psychol , 195U, 18, 
 53-58. "~ 
 
 23, Osgood, C. E, The nature and measurement of meaning, 
 
 Psychol. Bull ,, 1952, U9, 197-237. 
 
 2I|.. Postman, L, Perception, motivation and behavior, J« Person ., 
 1953, 22, 17-31. 
 
 25, Scodel, A, and Mussen, P. Social perceptions of authori- 
 
 tarians and nonauthoritarians. J. abncrm* soc. Psychol., 
 1953, U8, 181-18U. 
 
 26, Sopchak, A, L, Parential "identification" and "tendency 
 
 toward disorders" as measured by the Minnesota Multi- 
 phasic Personality Inventory. J. abnomi. soc. Psychol. , 
 1952, U7, 159-165. 
 
 27, Steiner, I, D, Ethnocentrism and tolerance of trait "incon- 
 
 sistency", J. abnorm. soc, Psychol. , 195U, U9, 3h9-35U» 
 
 28, Stone, G, C, and Leavitt, G. S, Generality of accuracy in 
 
 perceiving standard persons. Paper presented to Mid- 
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 29, Talland, G. A, The assessment of group opinion by leaders, 
 
 and their influence on its formation. J, abnorm. soc , 
 Psychol., 195U, i±2j i431-U3U. 
 
Il2 
 
 30, Wickman, E, K» Children's behavior and teachers' attitudes . 
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 31» Urigley, C. and McQuitty L. L, The square root method of 
 factor analysis: a re -examination and a shortened 
 procedure, Ilimeogrsphed, Univer, of Illinois, 19^3, 
 

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