The Kelley model as an analysis of variance analogy: How far can it be taken?

JOURNAL

OF EXPERIMENTAL

SOCIAL

PSYCHOLOGY

28,

475-490 (1992)

The Kelley Model as an Analysis of Variance Analogy: How Far Can It Be Taken? FRIEDRICH F~RSTERLING Clniversitiit

Bielefeld.

Bielefeld.

Germuny

Received November 20. 1991 Kelley’s (1967, 1973) suggestion that the attribution process can be compared to the statistical procedure of analysis of variance (ANOVA) was investigated. More specifically, I examined whether person and entity attributions vary as a function of different degrees of covariation of the effect with the respective cause, and whether certainty about the causal judgments is influenced by the number of observations. Unlike other studies in this field, complete rather than incomplete covariation information relevant for the ANOVA was presented. In addition, the numerical “raw” data rather than “prepackaged” (summary) data were given. Also, different degrees of covariation of the effect with the possible causes rather than merely two levels of covariation (e.g.. high vs low consensus) were presented to the subjects. The comparison of subjects’ attributions with the statistical parameters of the ANOVA revealed that causes were rated as increasingly important for an effect inasmuch as the variation attributable to the respective cause increased and the variation due to the alternative cause decreased. This relationship closely paralleled the effect-size parameter (q’). In addition, person attributions were more resistant to new information than entity attributions. Certainty ratings were Cl 1492 Academic Pren. Inc. uninfluenced by the manipulations.

Kelley (1967, 1973) has compared the process of attributing causality to the statistical method of analysis of variance (ANOVA). He argues that the possible causes (e.g., the person or the entity) for an event (e.g., failure) are treated by the naive attributor as independent variables and the events as dependent ones. Subjects are expected to compare the variation of the dependent variables that is brought about by one indeI thank Susan Fiske, Denis Hilton, Wulf-Uwe Meyer, Bernard Weiner, Rainer Reisenzein, and four anonymous reviewers for valuable comments on earlier drafts of the manuscript. I am especially grateful to Charles Judd who has made valuable suggestions based on a previous draft of the manuscript with regard to the data analyses. He also has made me aware of the fact that entity effects of the stimulus material correlate more strongly with attributions than person effects. Correspondence should be addressed to Friedrich Fiirsterling at Abteilung Psychologie. Universitlt Bielefeld 1,480O Bielefeld 1, Postfach 8640, Germany. 475 0022-1031/92 $5.00 Copyright Q IWZ by Academic Press, Inc. All rights of reproduction in any form reserved.

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TABLE INFORMATION

Entity 1 Entity 2

NECESSARY

1

FOR THE COMPUTATION

OF A NAIVE

ANOVA

Person 1

Person 2

A C

B D

pendent variable (e.g., the entity) to the variation that is brought about by the other independent variable(s) (e.g., the person) or, possibly, the total variation. Then, it should be expected that the effect is attributed to the possible cause(s) to the extent that it covaries with it (them). Hence, as the attribution is expected to result from a comparison of the variances accounted for by different causes (with each other or with the total variation), it should be comparable to statistical parameters of effect size such as 7)‘. Empirical tests of the Kelley model, however, have not yet fully explored the ANOVA analogy of the attribution process (see Cheng and Novick, 1990; Fdrsterling, 1988, 1989; Jaspars, Hewstone, and Fincham, 1983; Hilton and Slugoski, 1986). Most studies testing the Kelley Model have been guided by McArthur’s experiment (1972). Subjects were presented with scenarios that informed them about an effect (e.g., Tom failed at a task), consensus information (e.g., most individuals succeeded at the task), consistency (e.g., Tom always failed at this task), and distinctiveness (e.g., he also failed at other tasks). Subsequently, subjects were asked whether they believed that the effect was caused by the person or the entity. In several respects, the procedure introduced by McArthur (1972) does not fully explore the model as an analogy of ANOVA. First, the design of the “naive” ANOVA is not complete (see Cheng and Novick, 1990; Forsterling, 1989; Jaspars et al., 1983). For instance, consider the situation when the individual has to choose between an attribution to the person or to the entity and, for the simplest case, in which information about two persons and two entities is available. If the attribution process were to truly resemble the ANOVA, the subjects would need to group their observations within a 2 (persons) x 2 (entities) design (see Table 1). The attributor would have to compare the effect that the person has at the entity under consideration with the effect that the person has at the other entity (Cell A vs Cell C, distinctiveness). In addition, the attributor would have to compare the person’s effect at the entity with the effect of other persons at this entity (Cell A vs Cell B, consensus) and, the effect of the other person at the other entity (Cell D) would also have to be known. This information (in Cell D), however, is generally not provided to subjects in the typical McArthur study (this information has

THE KELLEY

MODEL

477

been addressed by Pruitt & Insko, 1980, and has been labeled comparison object consensus, while Hilton & Slugoski, 1986, refer to this information as “norms”). Hence, one could not expect subjects to make attributions consistent with ANOVA predictions if they are not fully informed about the relevant data. Secondly, in a typical McArthur-type study, subjects are presented with prepackaged information (e.g., “most other individuals succeeded at the task”) with just two degrees (high vs low) of consensus or consistency (see Alloy and Tabachnick, 1984). With this procedure, only the null hypothesis can be tested (i.e., whether low consensus information leads to stronger person attributions than no or high consensus information). However, an important additional assumption, which is implicit in the conceptualization of the attribution process as an analog to ANOVA, cannot be tested with this paradigm; that is, whether attributions about a certain covariation pattern actually follow or deviate from a normative model. The (ANOVA) model can make more specific predictions than, for example, that low consensus leads to more person attributions than high consensus. It predicts that different degrees of covariation of the effect with the potential cause lead to different degrees of attribution of the effect to the respective cause. Hence, studies using verbal descriptions of (high vs low) covariation information do not make full use of the precision of the underlying theoretical model. They do not investigate whether subjects’ attributions actually parallel parameters of effect size in the statistical ANOVA. Similarly, the ANOVA analogy has not yet been fully exploited, as it has not been tested whether different parameters of the ANOVA correspond to different psychological concepts in the attribution process. Do lay attributors have a concept of effect size (an analogy of 77’) and a concept analog to the significance level? Or, in other words, do laypersons, like scientists, determine causality on the basis of which factor accounts for the greatest degree of variance and are they more uncertain about their judgments when they are based on few rather than numerous observations (degrees of freedom)? Kelley (1973), addressing these later issues. writes: Of the various uses to which the analysis of variance conception of the attribution process can be put, one of the most important has to do with the phenomenology of attribution validity. Here we deal with a peculiar aspect of self knowledge How does a person know that his perceptions, judgments, and evaluations of the world are correct or true. (pp. 111-112)

The present studies were designed to shed light on these concerns. Study 1 examined whether subjects who are completely informed about the 2 (persons) x 2 (entities) ANOVA design in which an effect covaries

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TABLE 2 DATA PATTERNS USED IN THE STIMULUS MATERIAL IN THE PE CONDITION. OF THE 2 (PERSON) x 2 (GAME) ANOVA .Trial Peter

-

I Hans

Trial Peter

2

Trial

Hans

Peter

3

Trial

Hans

Peter

Trial

Hans

5

1

5 4

1 2

5 4 1

1 2 1

5 4 I

1 2 1

Video B

4

1

4 5

1 2

4 5 5

1 2 5

4 5 5

1 2 5

.96 .02 .02 .oo

.55 .04 .02 .39

.YO .oo .oo .lO

--

4

Video A

Person (P) Game (G) PxG Residual

AND $ VALUES

.33 .I5 00 .52

5

Peter

Hans

5 4 1 2 4 5 5 5

1 2 1 1 1 2 5 5 .22 .29 .oo .50

Note. n2 Values are Sums of Squares due to one Component divided by the total Sums of Squares in the Data. The stimulus material in the EP condition was identical with the exception that the labels “Person” and “Video” were exchanged.

in different degrees with the two involved causes attribute the effect to the involved cause(s) inasmuch as it covaries with them. In addition, it investigated whether subjective certainty about the correctness of an attribution corresponds to the significance level of the statistical ANOVA. In sum, I investigated whether the attribution process can truly be conceptualized as analogous to the central statistics of the ANOVA. In Experiments 1 and 2, this question was examined for two identical covariation patterns that were introduced as stemming from two different possible causes (person and entity). STUDY

1

Method Subjects. Subjects were 30 male and female students at the University of Bielefeld, Germany, who were paid for their participation. Experimental material. To minimize the influence of prior knowledge on the attribution process, subjects were told that the experiment was designed to analyze how individuals explain results in video games. They were informed that they were to rate, based on limited information, why a person had achieved a certain result (i.e., 4 or 5 penalty points) in a video game and how certain they were about their judgment. Subsequently, subjects were presented with the results (1 penalty point to 5 penalty points) that two persons (Peter and Hans) obtained in two unspecified video games (Game A and Game B). This information was provided in a tabular form, as in Table 2, for five consecutive trials, each on a separate page. On each trial, the last performance of Peter at Game B was especially marked, and subjects were instructed to indicate why this performance had occurred. In one condition,

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MODEL

479

the first two data patterns that subjects saw were characterized by a great amount of variation of the results due to the person, and, in the following three patterns, information was added that increased the variation due to the entity (“Person-Entity-Condition;” see Table 2). The second group of subjects received the same pattern of information with the only difference being that the labels “Person” and “Games” were exchanged (“Entity-Person Condition,” see Table 2). Hence, subjects in the two experimental conditions were confronted with identical patterns of variation, the one difference being that one group was led to believe that the person caused the specific variation, whereas the other learned that the very same variation was caused by the entity. Therefore, the experiment resulted in a 2 (Person-Entity versus Entity-Person information) X 5 (data patterns) experimental design with repeated measures on the last factor. Due to the numerical format of the data presented to the subjects, it was possible to conduct “statistical” analyses of variance on each of the five stimulus data patterns and to compare the central statistics of the “scientific” ANOVA with the attributions of the “naive” ANOVA. Table 2 presents the effect sizes ($ = Sum of Squares between divided by the total Sum of Squares) based on five 2 (person) x 2 entity ANOVA as calculated for the stimulus material, separately, for each trial. To assess the dependent variables, subjects were asked, following each scenario, to rate on a scale ranging from 6 (the game outcome was entirely due to the person) through 0 (neither the person nor the game) to 6 (the task outcome is entirely due to the game) which cause they perceived to be responsible for the outcome. The participants had to make attributions about the following outcome: Peter’s last performance at Game B (which was always a comparably negative outcome, i.e., 4 penalty points in Trial 1, and 5 penalty points in the remainder of the trials). In addition, subjects were asked to rate for each trial the degree to which they were unsure (1) or sure (7) about their causal judgment.

Results Figure 1 depicts the attributions that subjects made in the person to entity (PE) and the entity to person (EP) conditions. It shows that numerical covariation information that was not prepackaged influenced attributions markedly. A one-way repeated measures ANOVA revealed that data patterns characterized by an initially high variation due to the entity (and low person variation) resulted in stronger entity attributions than subsequent patterns that added person variation, F(4, 56) = 17.68, p < .OOl. With regard to the Person-Entity condition, results were less pronounced, although subjects made stronger person attributions when variation in the stimulus pattern was due to the person than when more entity variation was introduced, F(4, 56) = 2.77, p < .05. To determine the degree of congruence of subjects’ attribution ratings with the “scientific” (2 x 2) ANOVA of the stimulus data, correlations between the five mean attribution ratings (averaged across subjects) and the five predicted ratings (n2 of the factors “Person” and “Entity;” see Table 2) were calculated (separately for the PE and the EP condition). The correlation (between the five means) of the actual ratings and the (five) predicted ratings (7’) in the EP condition was r = .98, p < .Ol, for the entity q*, and r = - .94, , p < .Ol, for the person n2. The correlation of the five predicted ratings with the five actual ratings for each individual subject averaged across subjects was I = - .71 for the

480

FRIEDRICH ,3 rily

FORSTERLING

attribution

-165 Person = m

Tr 1

,

Tr 2 Tr 3 Trial

Tr 4

Tr 6

attribution Eta ep Eta pe

a m

Attribution Attribution

ep pe

FIG. 1. Mean attributions to the person and the entity for five trials in the Entity-Person (EP) and Person-Entity (PE) conditions and statistically predicted attributions ($ x 10 for entity and person factor).

person q2 and r = .74 for the entity nz (individual butions with person and entity g2, respectively, - .95/.97; 2, r = -.76/.98; 3, r -.92/.91; 6, r = -.93/.90; 7, r 11, -.87/99; 10, r = -.82/.99; r = - .61/.27; 14, r = - .36/.02;

correlations for Subject

of attri1, r =

= -.70/.93; 4, r = -.87/.90; 5, r = = -381.43; 8, r = -.95/.89; 9, r = r = -.62/.75; 12, r = -.50/.19; 13, 15, r = - .73/.93). Hence, for almost

all subjects in the EP condition, the actual attribution ratings correlated highly and consistently with the statistical n* values of the initially covarying factor (i.e., the entity in the EP condition and the person in the PE condition) and with those of the alternative cause (i.e., the entity in the PE condition and the person in the EP condition). Correlations between attribution means and predicted means were also high in the PE condition (r = - .83 n.s. with the person n2 and r = .99, p < .Ol with the entity 7’). Mean correlations averaged across individual subjects were r= - .27 for person q* and r = .33 for entity q* and hence, somewhat lower than in the EP condition (Subject 1, r = - .85/.74; 2, r = - .87/.98; 3, r = -.93/.95; 4, r = -.79/98; 5, r = .OO/.OO; 6, r = -.80/.98; 7, r = .97/-.80; 8 = -.38/.75; 9, r = -.57/.63; 10, r = -.77/.90; 11, r= -.83/.65; 12, r = .OO/.OO; 13, r = .OO/.OO; 14, r = .81/.95; 15, r = .95/ - .87).

To determine

whether covariation

information

influenced

subjects’ at-

THE KELLEY

MODEL

481

tributions differentially depending upon whether it was designed to lead initially to person attributions (and then to entity attributions) or vice versa, a 2 (Person-Entity vs Entity-Person Condition) x 5 (Trial) ANOVA with repeated measures on the last factor was conducted. To make the attribution ratings of the EP and the PE condition comparable, an index of attribution strength was needed that would not take account of the direction of the rating. Therefore, the attribution scale was recoded. For the PE condition, a large degree of attribution to the person (6) was coded as 13 and a large degree of attribution to the entity (6) as 1; in the EP condition, a large degree of attribution to the entity (6) was coded as 13 and a large degree of attribution to the person (6) as 1. Hence, the scale values reflected the degree to which the entity was seen as responsible for the effect in the EP condition in the same manner as the person was seen responsible in the PE condition. The ANOVA yielded a significant main effect for the condition factor, F(1, 28) = 11.78, p < .002, due to the fact that subjects tended to make stronger attributions to the person (M = 8.96) in the Person-Entity condition than attributions to the entity in the Entity-Person condition (M = 6.13). In addition, a strong main effect for the trial variable indicated that the information about covariation in the stimulus material significantly influenced the attributions of the subjects over trials, F(4, 112) = 16.33, p < .OOl. Finally, a significant interaction of the condition and trial factor, F(4, 112) = 3.52, p < .Ol, was due to the fact that subjects were more likely to assimilate their attributions to the new data in the Entity-Person condition (Ml = 8.67, M2 = 8.26, M3 = 6.60, M4 = 4.13, M5 = 3.00) than in the Person-Entity condition (Ml = 9.67, M2 = 9.60, M3 = 9.67, M4 = 8.53, M5 = 7.33). These data suggest that, given identical patterns of stimulus data about covariation, individuals have a stronger initial tendency to make person attributions, they more rigidly maintain the initial person attributions in the face of contradictory information, and are comparatively more willing to adapt their entity attributions to the new data pattern. With regard to the certainty ratings, none of the situational manipulations yielded significant results (F < 1). Subjects reported almost equal certainty (around M = 4.5) in all conditions. Discussion The present study has demonstrated that individuals can use numerical, nonprepackaged information to arrive at causal judgments as to whether an effect has been brought about by the person or by the entity. Moreover, these causal judgments are sensitive to variations in the stimulus data in a similar way as the “scientific” ANOVA. Not only is the effect attributed to the cause with which it covaried but it also is attributed to the respective causes with a magnitude that reflects the degree of covariation of the

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effects with the possible causes. These findings lend additional support to the notion that the attribution process can be conceived of as an analogy to the ANOVA and suggest that attribution strength can be conceived of as an analogy to the effect-size parameter in the statistical ANOVA. However, certainty ratings are uninfluenced by the situational manipulation. Subjects are equally certain about their causal judgments independently of whether the attribution is based on a few or several observations. It appears possible that subjects express high certainty about their attributions by giving an extreme rating on the attribution scale (instead of indicating high certainty) and low certainty by making a more moderate attribution (instead of indicating low certainty). In line with these speculations, correlations between attributions and attribution extremity and certainty ratings are insignificant. Therefore, in a second study, subjects were requested to make forced choices among attributions and then make their certainty ratings. It was expected that subjects who make the same attribution with regard to two data patterns that are similar regarding the effect size but differ with regard to degree of freedom will be more certain about their attribution with increasing degrees of freedom. STUDY 2

Study 2 was designed to investigate whether the more typically used forced-choice assessment of causal attributions would produce the same results on attributions as the rating scale employed in Study 1. More importantly, it tested the hypothesis that the certainty ratings would be influenced by the number of observations (given equal degrees of variation) when subjects made forced choices on attributions. This was expected as they now lacked the possibility of expressing different degrees of certainty about the attribution by making more or less extreme attribution ratings on the scales. For instance, subjects were expected to make entity attributions in the first two trials in the EP condition and person attributions in the first two trials in the PE condition. However, as the amount of variation in the first two trials in each condition was approximately equal (see Table 2) while being based on four observations in the first trial and on eight in the second trial, it was expected that certainty about the attribution would be higher in the second than in the first trial. Method Study 2 was a replication of Study 1 with the exception that subjects were forced to choose between a person or an entity attribution for the effects. Subjects were 40 students at the University of Bielefeld who were randomly approached in the library.

Results

Study 2 yielded quite similar results to Study 1 with regard to the attributions (see Table 3). To directly compare the results of the two

THE KELLEY TABLE FREQUENCIES

483

MODEL 3

OF PERSON AND ENTITY ENTITY-CONDITION

ATTRIBUTIONS FOR FIVE TRIALS AND THE ENTITY-PERSON-CONDITION

IN THE PERSON-

Trial 1

2

3

4

Person Entity

13 I

15 5

15 5

12

8

8

12

Person Entity

4

I

13

13

12 8

15

16

5

7

Condition Person-Entity

Entity-Person

5

studies, ANOVA were initially conducted on the frequency data. The frequency of entity and person attributions, respectively, differed in the EP condition, F(4, 76) = 5.37, p < .OOl, and in the PE condition, F(4, 76) = 2.94, p < .05, in the expected directions across trials (see Table 3). Furthermore, a 2 (conditions) x 5 (trials) ANOVA with repeated measures on the second factor revealed a significant interaction, F(4, 152) = 2.72, p < .03, (the respective analyses were based on similar recoding of the data to that in Study 1). As in Study 1, this was due to the fact that the frequency of person attributions decreased less rapidly in the PE condition across trial than the frequency of entity attributions in the EP condition. More specifically, when the effect covaried with the entity (Trials 1 and 2 in the EP condition), more entity than person attributions were made (x2 (1) = 7.20, p < .007, for Trial 1; and x2 (1) = 1.80, p = .18 for Trial 2). When information was added that was inconsistent with an entity attribution (Trials 3, 4, and 5), the frequencies of person attributions increased: More person than entity attributions were made in the latter three trials of the EP condition (x2 (1) = .80, n.s., Trial 3; x2 (1) = 5.00, p < .05, Trial 4; and x2 (1) = 1.80, p < .18, Trial 5). When variation was primarily due to the person, more person than entity attributions were made (x2 (1) = 1.80, p < .18, Trial 1; and x2 (1) = 5.00, p < .05, Trial 2). The increased variation in the stimulus data pattern attributable to the entity, however, did not increase the amount of entity attributions in Trials 3 and 4 in the same way as person attributions were increased in these trials in the EP condition. In fact, in Trial 3 there were still significantly more (x’ (1) = 5.00, p < .05) person than entity attributions in the PE condition, whereas in the same trial of the EP condition, the majority of individuals made attributions to the person. Furthermore, in Trial 4 more subjects in the PE condition attributed the effect to the person than to the entity, whereas in the same trial of the EP condition, subjects made significantly more attributions to

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,3 Entity attribution 1

10

5

0

-6

-10

-16’

I

I Tr 1 Person

attribution

I Eta ep m Eta pe

Tr 2 Tr 3 Trial

Tr 4

m Attribution a

Attribution

Tr 6 ep pe

FIG. 2. Frequencies of individuals who made attributions to the person in the PersonEntity (PE) condition and of those who made entity attributions in the Entity-Person (EP) condition and statistically predicted attributions (11’ x 10 for the person and entity factor, respectively).

the person than to the entity. Only in the last trial were more attributions to the entity than to the person made in the PE condition (x” (1) = .80, n.s.). In addition, the frequencies of attributions selected again paralleled the effect-size parameter of the stimulus material (see Fig. 2). Correlations between the five (statistically) predicted effect sizes and the actual frequencies of the attributions in the five trials were, r = .94, p < .Ol, with entity q2 and r = .67, n.s., with person v2 in the EP condition. In the PE condition they were r = .95, p < .Ol, with entity q2 and r = .69, n.s., with person q2. As in Study 1, however, the situational manipulation did not differentially influence certainty about the attributions. Two (Person-Entity vs Entity-Person pattern) x 5 (Trials) ANOVAs with repeated measures on the last factor revealed insignificant main effects and an insignificant interaction for certainty ratings in the PE condition, F(4, 76) = 1.31, n.s., as well as in the EP condition, F < 1. Even when the analysis included only subjects who made person attributions in both of the first two trials in the PE condition and entity attributions in the first two trials in the EP condition, differences on certainty ratings did not vary significantly from chance.

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MODEL

485

Discussion

Study 2 replicates Study 1 with regard to the major findings concerning attributions: when attributions are also assessed by a forced-choice methodology, the number of subjects who attribute an effect to a certain cause increases with the amount of variation of the effect with the respective cause and decreases with increased variation due to the alternative cause. In addition, there is a replication of the finding of Study 1 that person attributions (in the PE condition) decrease more slowly (across trials) with decreasing person variation and increasing entity variation than entity attributions in the EP condition, when entity variation decreases and person variation increases. Contrary to our initial hypothesis and consistent with the data of Study 1, however, certainty ratings are uninfluenced by the data patterns. Hence, it seems that certainty ratings are not an analogue to the significance level of the ANOVA in the same way as the attribution process itself can be conceived of as analogue to the effect size. STUDY 3

Study 3 was designed to test whether the main results of the previous experiments would generalize when different contexts and experimental designs were used. More specifically, it was investigated whether causal judgments are also influenced by numerical information about covariation when (a) a between- rather than within-subject design is used, (b) the effect to be explained is taken from a different content area, (c) person and entity attributions are assessed separately, (d) the effect to be attributed is a successful rather than an unsuccessful event, and (e) when different numerical values are used. Method Subjects and experimental material. Eighty-five introductory psychology students were tested in a large group setting at the beginning of a lecture. Each subject received one sheet of paper on which they were instructed to indicate why a stimulus person had achieved a (good; 8 out of 9 points) result at an unspecified party game that was “new on the market.” They were asked to base their causal judgments on information about the points attained by two persons (Peter = P, and Hans = H) on two different games (Game A and Game B). This information was presented in a tabular form, similar to the previous experiments. Subjects were assigned to one out of four conditions: In the first condition, performance variation was almost exclusively due to the person (Peter received 6 and 7 points at Game A and 7 and 8 points at Game B whereas Hans received 1 and 2 points at Game A and 1 and 2 points at Game B). In the second condition, performance variation was largely due to the person, however, to a lesser degree than in Condition 1 (PA = 7, 7; PB = 2, 8; HA = 2, 6; HB = 1, 1). In the third condition, variation was largely due to the entity (PA = 2, 6; PE = 7, 8; HA = 1, 1; HB = 2, 7), and in the fourth, variation was almost exclusively due to the entity (PA = 1, 2; PB = 7. 8; HA = 2, 1; HB = 6, 7). To assessthe dependent variables, subjects were asked to rate, on separate scales ranging

486

FRIEDRICH 151

Person

FORSTERLING

attribution

t

-15

’

I Cond

Entity

1

Cond

2 Cond Condition

3

Cond

attribution

m Eta person Ef%EI Eta entity

4 -

m m

Attribution Attribution

person entity

FIG. 3. Person and entity attributions and person-r)’ values ( x 10) for stimulus data patterns characterized by variation almost exclusively due to the person (Condition l), largely due to the person (Condition 2), largely due to the entity (Condition 3). or almost exclusively due to the entity (Condition 4). from “unimportant cause” (1) to “important cause” (7). the perceived importance of stable dispositions of the person and properties of the game (entity) as causes for Peter’s last result on Game B (8 points).

Results Person and entity attributions were subjected to separate (oneway) ANOVA with (4) conditions being the independent variable. Figure 3 indicates that subjects attached causal importance to the person inasmuch as the effect (in the stimulus data) covaried with the person, F(3, 81) = 3.92, p < .02: The person was considered as the most important cause in the condition in which the effect almost exclusively covaried with the person. The person was perceived as less important in the condition in which the effect largely covaried with the person, even less important when the effect covaried mostly with the entity, and least important when the effect covaried almost exclusively with the entity. Similar results were obtained for entity attributions. They, too, were higher when the effect covaried largely or almost exclusively with the entity than when the effect covaried with the person, F(3, 81) = 2.30, p < .09). However, the rank

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ordering of the means of the entity attributions was not perfectly consistent with the predictions, as (insignificantly) more causal importance was attached to the entity in the condition in which the effect covaried largely with the entity than in the condition in which the effect covaried almost exclusively with the entity. Furthermore, there were significant correlations between attributions and the $ values of the stimulus data ($ = .953/.008, .386/.197, .285/386, .008/1.953, for Conditions 1 to 4 and for the factors “person” and “entity”, respectively). Subjects’ person attributions correlated with the $ values of the person factor, r (85) = .31, p < .Ol, and the r)’ values of the entity factor, r = .32, p < .Ol; and entity attributions correlated with the q2 value of the person factor in the stimulus-material, r (85) = - .22, p < .05, and with the entity $, r = .13 n.s. Discussion

The results of the third study indicate that causal judgments are-as in Experiments 1 and 2-also sensitive to degrees of covariation information when a between- rather than a within-subject design is used and when the effect to be explained was a “good” rather than a “bad” result. In addition, similar results to those in the previous experiments are obtained when person and entity attributions are assessed separately and when different numerical values are used in the stimulus material. However, the correlations between statistically predicted and actual attributions in Experiment 3 are weaker than in the studies using repeated-measure designs. This may be due to the increased error variance in the betweensubject design study and/or the possibility that initial attributions in the repeated-measure design provided subjects with a baseline judgment for the remaining attributions. GENERAL DISCUSSION

The present studies differ from other experiments investigating attribution processes in two respects: First, subjects did not receive verbally prepackaged information about frequencies of effects occurring in the presence or absence of possible causes. Instead, they were informed about different degrees of effects (e.g., “1 to 5 penalty points”) in connection with different possible causes. Second, the information provided covered all of the 2 x 2 cells of a naive ANOVA. Results of all three studies indicate that individuals attach importance to a cause inasmuch as the effect covaries with it, or, inasmuch as the effect does not covary with the alternative cause. If a possible cause “creates” a great amount of variation, high causal importance is attached to it, and low causal responsibility is allocated to it if the effect does not vary (or hardly varies) with its presence and absence. On the most general level, these results indicate that individuals are, under certain (possibly

488

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idealized) circumstances (i.e., when attributionally relevant information is presented in a tabular form), quite able to base their attributional judgments on (a) numerical (nonprepackaged) data that include (b) all of the information relevant for the naive 2 x 2 ANOVA (see Shanks, 1989, for conditions that might reduce or modify such an influence). On a more specific level, the data indicate that the “naive” causal analysis yields results in response to sets of stimulus data that are quite comparable with the scientific treatment (computation of effect size) of the same data. This further supports the idea that the attribution process can be conceptualized as analogous to the ANOVA and, on a more general level, that human causal inferences quite closely follow “rational” models and have a remarkable degree of accuracy. The comparison of subjects’ attributions with selected parameters of the ANOVA makes it possible for us to shed light on the question of whether attributions are “accurate” or “biased” from a new perspective. Let us consider two well-established attributional biases, that is, the “fundamental attribution error” (see Ross, 1977) and the underuse of consensus information (see for a summary Kassin, 1979) from the perspective of our present results. In addition to the remarkable consistency of attributions and the ANOVA parameters, results also indicate that “content-free” covariation information is not the only determinant of the attribution process. Attribution ratings in response to identical data patterns differ as a function of whether variation of an effect is introduced as stemming from the person or from the entity (see Studies 1 and 2). Subjects in both studies tend to make generally stronger person attributions than entity attributions and they revise entity attributions more readily when covariation due to the person increases (and entity caused variation decreased) than they revise person attributions when variation due to the entity increases (and variation due to the person increased). Hence, they appear to be exhibiting the “fundamental attribution error” (see Ross, 1977). Although the studies (1 and 2) were not designed to investigate the possible mechanisms underlying such an effect, it appears conceivable that, following a “person” data pattern, subjects attribute increased variation of the effect with the entity to effort, hence allowing them to maintain a person attribution. The possibility of introducing an additional entity factor that might account for the increased person variation in the “entity” pattern might not have been similarly available to the subjects. An additional aspect of the present studies relevant to the question of biases concerns the relative importance of consensus information for causal judgments. If the frequently reported underuse of consensus information (see Kassin, 1979) were to generalize to the present paradigm, we would expect identical covariation information to yield less powerful effects when it is introduced as stemming from the person (consensus) rather than the

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entity (distinctiveness). In the stimulus material, the person q2 is equivalent to consensus and the entity n2 to distinctiveness. As the magnitude of one parameter is not perfectly inverse to the other (e.g., r = - .88 between person q2 and entity n2 in the PE condition), one can ask which of these two parameters correlates more strongly with the attributions. Interestingly, in both (PE and EP) conditions in Studies 1 and 2, mean attributions correlate more strongly across trials with the entity effect than with the person effect. Also, when averaging correlation coefficients between effect size parameters and attributions for individual subjects, entity effects have stronger correlations with attributions than person effects. Although all correlations are relatively high and differences between correlations of attributions and entity and person effects, respectively, are not significant, these differences are surprisingly consistent. Hence, there seems to be suggestive evidence that attributions might be more strongly influenced by entity than by person covariation, thus suggesting that subjects might also exhibit an underuse of consensus in the present paradigm. Furthermore, the present studies have failed to demonstrate that the certainty about attributions covaries with any parameter of the stimulus data. Further research will need to determine when individuals make certainty estimates about their attributions and which mechanisms determine and change these judgments. The present research has-like most research in this tradition-not addressed the cognitive operations and processes responsible for the formation of causal judgments in response to numerical data patterns. My primary purpose is to systematize the information that might enter this process and to determine the impact of this information on the outcome of this process. Different methodologies would need to be used to investigate cognitive operations themselves. Naturally, I do not want to suggest that subjects calculate the sums of squares and divide them. However, it is conceivable that individuals have concepts such as “small” or “large” degrees of variation, and that they use these in a way consistent with the basic notion of the effect size to causally interpret numerical data patterns (see Cheng & Novick, 1990). REFERENCES Alloy, L. B., & Tabachnik, N. (1984). Assessment of covariation by humans and animals. The joint influence of prior expectations and current situational information. Psychological Review, 91, 112-149. Cheng, P. W., & Novick. L. R. (1990). A qualitative contrast model of causal induction. Journal

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F. (1988). Attribution theory in clinical psychology. Chichester: Wiley. F. (1989). Models of covariation and causal attributions. How do they relate analysis of variance. Journal of Personality and Social Psychology 57, 615-625. (1958). The psychology of interpersonal relations. New York: Wiley, M., & Jaspars, J. (1987). Covariation and causal attribution: A logical model

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of the intuitive analysis of variance. Journal of Personality and Social Psychology, 53, 663-672. Hilton, D. J.. & Slugoski, B. R. (1986). Knowledge based causal attribution: The abnormal conditions focus model. Psychological Review, 93, 75-88. Jaspars. J. (1983). The process of causal attribution in common sense. In M. Hewstone, (Ed.), Attribution theory: social and functional extensions (pp. 28-44). Oxford, England: Blackwell. Jaspars, J., Hewstone, M., & Fincham, F. D. (1983). Attribution theory and research: The state of the art. In J. Jaspars, F. D. Fincham, & M. Hewstone (Eds.). Attribution theory and research: Conceptual, developmental and social dimensions (pp. 3-36). London: Academic Press. Kassin, S. M. (1979). Consensus information, prediction and causal attribution: A review of the literature and issues. Journal of Personality and Social Psychology, 37, 19661988. Kelley, H. H. (1967). Attribution theory in social psychology. Nebraska Symposium on Motivation, 15, 192-238. Kelley, H. H. (1973). The process of causal attributions. American Psychologist, 28, 107128. McArthur. L. A. (1972). The how and what of why: Some determinants and consequences of causal attributions. Journal of Personality and Social Psychology, 22, 171-193. Mill, J. S. (1973). System of logic. In J. M. Robson (Ed.). Collected works of John Stuart Mill (8th ed., Vol. 7 & 8). Toronto: University of Toronto Press. (Original work published 1872). Orvis, B. R.. Cunningham. J. D., & Kelley. H. H. (1975). A closer examination of causal inference: The roles of consensus. distinctiveness and consistency information. Journal of Personality and Social Psychology. 32, 605-616. Pruitt. D. J., & Insko. C. A. (1980). Extension of the Kelley attribution model: The role of comparison object consensus, target object consensus, distinctiveness. and consistency, Journal of Personality and Social Psychology, 39, 39-58. Ross. L. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. In L. Berkowitz (Ed.). Advances in Experimental Social Psychology (Vol. 10, pp. 173-220). New York: Academic Press. Shanks, D. R. (IYSY). Selectional processes in causality judgment. Memory and Cognition, 17, 27-34.