Mood, categorization breadth, and performance appraisal: The effects of order of information acquisition and affective state on halo, accuracy, information retrieval, and evaluations

ORGANIZATIONAL

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42, 224

(1988)

Mood, Categorization Breadth, and Performance Appraisal: The Effects of Order of Information Acquisition and Affective State on Halo, Accuracy, Information Retrieval, and Evaluations ROBERT C. SINCLAIR The Pennsylvania

Srate University

Primacy effects and variations in mood affect impression formation. These biases should also affect performance appraisal. The present study investigated the effects of order of information acquisition and mood state on performance appraisal decisions. Recent theoretical research suggests that subjects in depressed moods should display less halo and greater accuracy in judgments than subjects in elated moods, since subjects in good moods have been shown to be broader categorizers. Subjects in good moods should make more positive evaluations and retrieve more positive and less negative information than subjects in bad moods (mood congruency). Order of information acquisition should result in primacy effects. Subjects initially received either a greater proportion of positive or negative behavioral information regarding a target’s performance. In a supposedly unrelated second study, subjects were put into either elated, neutral, or depressed mood states. Subjects wrote a brief description of their impressions of a target, evaluated the target on four global measures and on eight specific behavioral scales (evaluation measures), and finally recalled all the behavioral information that they could (memory measures). The predicted main effects for order of information acquisition and mood were confirmed, but no interactions were seen, suggesting that encoding and retrieval biases may act independently. Subjects in depressed states displayed the least amount of halo and greatest accuracy. The results are discussed in terms of encoding and retrieval biases, as well as categorization breadth and automaticity in performance appraisal. Cautionary notes re0 1988 Academic garding the generalizability of the findings are also discussed. Press, Inc.

Throughout code various

the course of performance review periods, supervisors entypes of performance information about their subordinates.

I thank Rich Carlson, Jim Farr, and Mel Mark for their careful readings and suggestions regarding the manuscript and Caran Colvin, Dan Ilgen, Rick Jacobs, Mark Miller, Jules Thayer, Jim Wilson, and three anonymous reviewers for their constructive comments on previous drafts. Special thanks to Phil Phelan, Leslie Sands, and Sandy Vannote for editorial advice, Sarah Benson and Krista Freeman for serving as confederates and judges, and Carolyn Hendrickson and Steve Motowidlo for their comments and aid in collecting and evaluating the critical incidents. The research described here served as a minor project required for partial fulfllment of the author’s Ph.D. Correspondence should be addressed to 22 0749-5978/88 $3.00 Copyrieh~ All rights

0 ,988 by Academic Press, Inc. of reproduction in any form reserved

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This information varies in its degree of positive and negative affective content. Since mood state varies, it is unlikely that a supervisor would be in the same mood when making performance appraisal judgments as when the information was encoded (this, of course, depends on the distribution of mood within persons). This fact has implications for the outcome of the performance appraisal process. There has been a recent surge in the amount of research directed toward the effects of mood state on the encoding, storage, and retrieval of information (e.g., Bower, 1981; Clark & Fiske, 1982; Clark & Isen, 1982; Isen & Daubman, 1984; Isen, Shalker, Clark, & Karp, 1978; Johnson & Tversky, 1983). It would not be surprising to find that mood state affects information retrieval in performance appraisal judgments in a manner consistent with mood congruency effects (Bower, 1981; Isen et al., 1978; Snyder & White, 1982; Teasdale & Fogarty, 1979; Teasdale & Russell, 1983). That is, judges in positive affective states should retrieve more positive and less negative information about target individuals, relative to judges in negative states. This, in turn, should lead to more positive performance ratings for targets rated by judges in positive affective states. More interesting, however, would be effects of mood state on halo error and accuracy, as suggested by recent evidence that mood affects categorization breadth (e.g., how likely we are to see a stimulus as falling into a particular category, or two or more stimuli as being similar). Isen and Daubman (1984) found that positive affective states lead to greater categorization breadth when compared to a control group. Isen and Daubman suggest three possible mechanisms through which positive affect could result in broader categorization: (a) “positive affect may prime an affective dimension of material that is not normally seen as affective and this in turn serves to change or broaden the relations perceived” (p. 1212); (b) positive affect promotes heuristic use (p. 1213); and (c) positive affect may result in “more material and more diverse material” coming to mind (p. 1213). Sinclair and Mark (1986a, April), as well as O’Malley and Davies (1984), have explored the effects of mood state on perceptions of fairness of reward allocation and on actual reward allocation, respectively. These studies (and the present study) were concerned with phenomena (equity) or stimuli (job performance) that are hypothesized to be affectively laden. Isen and Daubman use stimuli that are generally considered to be affectively neutral. Thus, Isen and Daubman’s first hypothetical mechanism is unlikely to apply in the cases of Sinclair and Mark and

Robert C. Sinclair, 203 Sloan Hall, Department of Psychology, Central Michigan University, Mt. Pleasant, MI 48859.

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O’MalIey and Davies (nor in the present case). Sinclair and Mark and O’Malley and Davies find that subjects in negative affective states are more likely to perceive equity as fair, and to allocate reward in a manner consistent with equity principles, than are subjects in elated states. These findings call Isen and Daubman’s third hypothetical mechanism into question in that negative states seemto be resulting in greater attention to, and weighting of, information and more discrete, algorithmic judgments than the judgments peculiar to elated states. However, it could be argued that positive affect results in less adherence to equity principles since elated subjects create information and reasons for differential performance that override equity. While this position is plausible, it lacks the parsimony of the heuristic hypothesis. The Sinclair and Mark and O’Malley and Davies effects are consistent with Isen and Daubman’s second hypothetical mechanism. Positive states may lead to greater heuristic/automatic-like processing resulting in broad categorization and less differentiation across performance levels. This would lead to less adherence to, and endorsement of, equity principles. Conversely, negative affective states may result in more algorithmic/controlled-like processing leading to narrow categorization, less error (if we consider reward allocation based on equity as accurate), and greater differentiation across performance levels. This would lead to greater adherence to, and endorsement of, equity principles. Sinclair and Mark (1986a, April) found that subjects in elated moods have less range in their judgments of social justice, relative to subjects in depressed states (the terms “depressed states” and “depression” are used to represent transient negative affective states, not chronic depression, in this paper; this usage is analogous to that of Velten, 1968). Subjects in depressed states made more discrete justice judgments across a range of stimuli. They seem to make more highly differentiated categorizations and do not assimilate as much information into existing categories, when compared to elated subjects (see Sinclair & Mark, 1986a, April, 1986b). Sinclair and Mark (1986a, April) suggest that this categorization effect also accounts for the results of O’Malley and Davies (1984), who found that subjects in good moods were unaffected by differential performance feedback when making reward allocations. However, subjects in bad moods made reward allocations consistent with relative performance. Thus, subjects in positive states may not distinguish performance levels, whereas subjects in negative states make use of narrower categories, leading to the use of discrete performance information in reward allocation. These effects would suggest that depressed subjects should display least halo (narrow categorization) and greatest accuracy in decision making based on algorithmic processing.

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The Sinclair and Mark (19&a, April) effect has two implications in the area of performance appraisal. First, mood should affect halo. Halo may result from not discriminating across performance dimensions or inappropriately categorizing job-related behaviors together. Thus, judges in depressed states should display less halo when compared to judges in positive affective states. This prediction is easily understood: Judges in good moods make broad categorizations, differentiate less, and assimilate more information into fewer categories, or into existing categories. They inappropriately categorize different job-related behaviors in the same performance category. This results in more halo. In contrast, subjects in depressed states make more narrow, discrete categorizations, discriminate among behaviors, and see them as falling into different performance categories. They make more specific judgments, resulting in less halo. Neutral moods were predicted to lead to levels of halo that fall between the levels of the elated and depressed groups since neutrality implies moderate levels of affect (not positive or negative). These predictions were made since Sinclair and Mark (1986a, April, 1986b) find only elationdepression differences in categorization breadth and O’Malley and Davies (1984) find more profound elation-depression differences than elationneutral differences; however, it should be noted that Isen and Daubman (1984)find only elation-neutral differences in categorization breadth. Second, subjects in depressed states should display greatest accuracy in performance appraisal judgments since they make more discrete categorizations. This should also result in greater predictability of global evaluations from specific ratings for depressed subjects who may integrate more diverse information into global evaluations. It may be the case that the performance judgments of those in depressedstates may be characterized by less halo, greater accuracy, and more validity.’ Order of information acquisition should affect performance judgments. Primacy effects in socialjudgments have been shown in numerous studies (Asch, 1946; Far-r, 1973; Far-r & York, 1975; Jones & Goethals, 1972; Jones, Goethals, Kennington, & Severence, 1972;Luchins, 1957;Springbett, 1958). Many of these effects can be conceptualized as affective biases in encoding of information. For example, encoding initial positive information tends to result in more positive global impressions of a target, relative to encoding initial negative information. Thus, encoding initial positive information about an employee prior to encoding subsequent negative information should result in positive performance appraisals and retrieval of more positive information about the target, relative to encod’ These predictions may depend on the nature of the true correlations among performance dimensions.

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ing initial negative information. This is important since there is no objective difference in the total information encoded. There are two competing positions regarding the potential interaction between the primacy and mood manipulations: (a) encoding specificity, which would lead to the prediction of an interaction; and (b) independent accessibility at input and accessibility at output effects. Congruent with encoding specificity (Bower, 1981; Flexser & Tulving, 1978; Tulving & Thomson, 1973; cf. Isen et al., 1978), mood state at retrieval could act as a retrieval cue based on the affect associated with the information that was initially encoded. Thus, the mood manipulation (retrieval bias) may affect the information retrieval, and evaluations, of those who encoded initial mood-consistent information to a greater degree than those who encoded initial mood-inconsistent information (encoding bias). That is, based on encoding specificity, mood congruency effects should be strongest for subjects who encode initial information and form initial impressions consistent with their affective state at retrieval. For example, there are three reasons why a subject who encodes initial positive information and subsequently retrieves information in a good mood should retrieve relatively more positive information: (1) primacy effects; (2) mood congruency; and (3) encoding specificity. Similarly, this rationale applies to why this type of subject should make the most positive evaluations. Alternatively, the primacy manipulation could affect accessibility at input (Higgins & King, 1981; Higgins, Rholes, &Jones, 1977; Sinclair, Mark, & Shotland, 1987), and thus the categories into which the information is encoded (and affective labels associated with those categories). The mood manipulation might affect accessibility at output (Anderson & Pichert, 1978; Higgins & King, 1981; Higgins & Rholes, 1978), thus independently affecting the type of information that is retrieved. Given this position, the primacy and mood manipulations should not interact. The target for performance appraisal in this study was a fictitious university professor, teaching introductory management at a business school. Subjects read 16 positive and 16 negative behavioral statements about the target. Order of information acquisition was varied such that subjects received either a greater proportion of initial positive or initial negative information. Subjects then participated in a purportedly unrelated second study, validating a mass administration of a mood induction procedure. Subjects were put into elated, neutral, or depressed moods. Finally, subjects wrote an open-ended description of their impression of the teacher about whom they had read, evaluated him on four global dimensions and on eight specific behavioral scales, and retrieved all of the behaviors that they could. The results of the study were interpreted in light of encoding and retrieval biases, categorization breadth, and automatic versus controlled processing in performance appraisal.

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METHOD Subjects The subjects were 60 female and 57 male introductory psychology students at The Pennsylvania State University. All were volunteers who received extra credit toward their final grades. Procedure Subjects arrived in groups of 15 to 25 for two purportedly unrelated studies. One study seemingly assessed a new method of performance appraisal (teacher evaluations). The second study apparently validated a group administration of a mood induction procedure. Ordering of the conditions was randomized within blocks. There were between 16 and 22 subjects per cell (all analyses corrected for the unequal cell sizes). Subjects were greeted by the experimenter who conducted the performance appraisal study (Experimenter 1). She introduced herself, gave a brief description of her procedure, and introduced the second experimenter (Experimenter 2), who gave an overview of his mood induction study. Experimenter 1 explained that she was interested in performance appraisal. She indicated that subjects would read a description of a particular teacher’s behaviors, both in and out of the classroom, and then they would evaluate a new performance appraisal metric that made use of the behavioral information provided. Subjects were told that the professor’s students were asked to provide both good and poor examples of this teacher’s behaviors. Subjects believed that a random sample of behavioral examples were selected for the study (the behaviors were actually critical incidents derived by having subjects in another study provide examples of good and bad teaching behaviors, both in and out of the classroom; Harari & Zedeck, 1973). Subjects were led to believe that the performance appraisal instrument was in its developmental stages and that they were helping to refine it. Subjects read a set of instructions describing the teacher’s background and then read each of 32 behaviors, once only. After reading each statement, subjects placed the packet of behaviors on the desk beside them and waited for further instructions. This took approximately 12 mm. Subjects were then given 5 min to complete a bogus questionnaire assessing the use of behavioral information and student involvement in teacher evaluations and promotion decisions. This questionnaire included four Likert items, three dichotomous items, and one open-ended question. Order ofinformation acquisition. The order of behaviors in the packets served as one independent variable. Subjects read either a greater pro-

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portion of initial positive or negative information. For the initial positive information condition, the order of behaviors was as follows: 8 positive, 2 negative, 1 positive, 2 negative, 1 positive, 2 negative, 1 positive, 2 negative, 1 positive, 2 negative, 1 positive, 2 negative, 1 positive, 2 negative, 1 positive, 1 negative, 1 positive, and 1 negative. For the initial negative information condition, the ordering of behaviors was the inverse (8 negative, 2 positive, 1 negative, . . . , and 1 positive). The behavioral items represented eight evaluative categories, each containing four behaviors with varying degrees of positive and negative information. The behavioral categories and number of positive and negative items in each were as follows: (1) answering questions; with 0 positive and 4 negative behaviors; (2) delivering lectures; containing 1 positive and 3 negative behaviors; (3) preparation and organization; containing 1 positive and 3 negative behaviors; (4) availability; containing 2 positive and 2 negative behaviors; (5) sensitivity; containing 2 positive and 2 negative behaviors; (6) friendliness; containing 3 positive and 1 negative behavior; (7) setting expectations; containing 3 positive and 1 negative behavior; and, (8) generating student involvement; containing 4 positive and 0 negative behaviors. The varying degrees of positivity and negativity in each category served to help explore the halo and accuracy issues. (For the purposes of the present study, halo was conceptualized as the magnitude of interdimension correlation. This explication of halo is commonly used although it is not the only definition; see Cooper, 1981; Saal, Downey, & Lahey, 1980.) An independent validation of the individual stimuli indicated that the behaviors and behavioral categories reflected the valence for which they were intended (n = 103). The positive items were seen as more positive than the negative items, t(102) = 50.55, p < JO1 (M = 4.27, internal consistency estimate based on Cronbach’s cx = .77 and M = 1.67, cx = .73 for the positive and negative items, respectively; ratings were on a 5-point scale ranging from l--extremely negative to 5-extremely positive). The positive items (M = 4.14, (Y = .76) differed from the negative items (M = 1.81, a = .82) on ratings of teaching effectiveness, t(102) = 38.08, p < .OOl(5-point scale ratings ranging from l-extremely ineffective to 5-extremely effective). There was also a small, but significant, difference on perceived importance of the behaviors with positive behaviors (M = 3.97, OL= .73) rated as slightly more important than negative behaviors (M = 3.54, cy = .94), t(102) = 4.41, p < .Ol (5-point scale ratings ranging from l-extremely unimportant to 5-extremely important). A word count indicated that there was no difference in the number of words read per behavior, t(31) = 1.02, ns, (M = 42 and M = 38 words per positive and negative behavior, respectively). This would indicate

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that the stimuli adequately represent the manipulation for which they were intended.2 Mood induction. Experimenter 1 thanked subjects for their participation and turned the sessionover to Experimenter 2, who told subjects that he was validating a mass administration of the Velten (1968) mood induction procedure. They were told that the mass administration procedure had not yet been attempted and that the experimenter was planning to use it for future research. It was emphasized that they should respond very honestly on the subsequent mood measure since using an induction that was not truly altering mood state would create problems for future research. Subjects read a set of instructions that were like the standard Velten (1968) instructions, but differed in two ways: (1) subjects were asked to read and write out each statement and to concentrate on each until the experimenter asked them to go on to the next; (2) following the last statement were sets of incubation instructions that asked subjects to concentrate on events in their own lives that made them feel like the mood represented by the statements; they were asked to do this with their eyes closed for 3 min. Subjects were also told that if writing the statements was fatiguing or interfered with building the mood, they should stop writing and just concentrate on the statements (Sinclair & Mark, 1985). The in’ The behavioral stimuli were initially developed for a study concerning automatic versus controlled processes in performance appraisal (Sinclair, Hendrickson, & Motowidlo, 1985). The behaviors were assigned to categories based on three judges’ sorting the behaviors into categories. Only behaviors that were assigned to the same category by all three judges were used in the study. The categories were named after the sorting task, based on agreement among the judges. The independent group of subjects (n = 103) was also asked to assign each behavior to one of the eight behavioral categories. They were asked to provide both their first and second choices for the assignments. There was between 79 and 99% agreement with the a priori category assignments (M = 92.5%). This indicated that the behaviors did reflect the constructs for which they were intended. (It should be noted that 13 different random orders of the behaviors were used in the validation study.) The difference in perceived importance was small relative to the differences in perceived valence and perceived effectiveness. The behaviors were selected to be equal in importance; however, during debriefing some subjects reported that they did not discriminate among the three rating scales and inappropriately rated negative behaviors as unimportant. These subjects could account for the small difference in the importance ratings. Valence scores were computed for each category based on the independent group’s ratings of the individual behaviors (the mean of the valence ratings for the four behaviors in each category). The mean ratings corresponded to the a priori valence assignments: generating student involvement (M = 4.40); setting expectations (M = 3.47); friendliness (M = 3.46); availability (M = 3.05); sensitivity (M = 2.73); preparation and organization (M = 2.65); delivering lectures (M = 2.44); and, answering questions (M = 1.57). All of these means differed from one another at the p < JO1 level (all t[l02]‘s between 6.10 and 46.88), except sensitivity and preparation and organization, r(102) = 1.74, p < . 10, and friendliness and setting expectations, r(102) = .32, ns.

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cubation period was included since Borkovec, Robinson, Pruzinsky, and DePree (1983) found that a short incubation period greatly enhanced changes in affective state. The subjects read either the 60 Velten elation, neutral, or depression statements3 at the rate of one per 30 s, followed by the 3-min incubation period. The statements were typed individually on 1.5 x 8.5 in. cards that were stapled together in a booklet. The last page of the booklet contained the incubation instructions. Following the incubation period, subjects were asked to complete a mood measure comprised of eight 7-point bipolar items. These items, which were anchored at 1 and 7 for the first and second anchor, respectively, were (1) very passive-very active; (2) very sad-very happy; (3) very depressed-very elated; (4) very worriedvery serene; (5) very tense-very relaxed; (6) very hostile-very peaceful; (7) very angry-very content; and (8) very excited-very tranquil. There was a neutral point at 4 on all scales. Items 5 and 8 were reversescored for all analyses. After subjects completed the mood measure, Experimenter 1 thanked them and left the lab. This procedure took 40 min. Dependent measures. Experimenter 1 told subjects that she was also interested in the effects of time delay on memory, and because of this she had left one questionnaire for the end of the session. She explained that some groups of subjects completed this questionnaire immediately following the reading of the teacher behaviors, but that the present group had been chosen for the delayed condition. Subjects were given 3 min to write a brief open-ended description of their impressions of the teacher. They were then given 5 min to complete 12 Likert items. The first 4 items were global evaluations of the teacher on 7-point scales. The items addressed (1) overall teaching effectiveness (Overall, how effective do you think Professor Newport was as a teacher?) (anchored at 1 [very ineffective], 2 [moderately ineffective], 3 [slightly ineffective], 4 [neither ineffective nor effective], 5 [slightly effective], 6 [moderately effective], and 7 [very effective]); (2) a tenure decision (Should he be granted tenure?) (anchored at 1 [definitely no], 4 [can’t decide], and 7 [definitely yes]); (3) a promotion decision (Should he be promoted to associate professor?) (anchored at 1 [definitely no], 4 [can’t decide], and 7 [definitely yes]); and (4) a pay increase decision (How much of a pay raise should he receive this year?) (anchored at 1 [O%l, 2 [3%1, 3 [6%], 4 [9%1, 5 [12%1, 6 [15%], and 7 [18%]). The next eight items were directed toward the specific categories that the behaviors were chosen to reflect. Each category was described. For example, “answering questions” was described 3 The Velten was modified to remove reference to the Vietnam war. This reference was replaced with a statement involving nuclear war. A reference to “God” was also altered to remove the word “God.”

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as “providing opportunities for students to ask questions; answering questions willingly and thoroughly without embarrassing students who ask them; not getting defensive when answering questions.” Each category and description was followed by a 7-point scale anchored at (1) very ineffective, (3) slightly ineffective, (5) slightly effective, (7) very effective. Finally subjects were given 5 min to recall (free) all of the behaviors that they could. Debriefing. Once subjects had completed the dependent measures, Experimenter 2 returned to the lab and asked subjects to write answers to two more questions. These questions were in the form of a funnel-type debriefing questionnaire used to determine hypothesis suspicion. Subjects were asked (1) Was there anything that affected your responses to the mood measure, other than the statements that you read? And (2) What was I trying to do with my mood induction? Experimenter 1 asked subjects to write answers to the following 2 questions: (1) Was there anything that affected your responses to my questionnaires other than the behaviors that you read? And (2) What do you think I was studying? Two subjects were identified as hypothesis suspicious and were excluded from the data analyses4 Finally, subjects were debriefed and given their extra credit. Subjects in the depression condition were exposed to the elation induction prior to leaving the lab. RESULTS Manipulation check. The mood measure was subjected to a.principal axis factor analysis with an oblique rotation. Based on a scree test, 2 factors were identified. These were (1) affect, comprised of items 2, 3, 4, 6, and 7; and (2) activity, comprised of items 1 and 8. The two factors accounted for 80.20% of the variance and were uncorrelated, r = .15. Cronbach’s estimate of internal consistency was computed for both scales. These were .87 for the affect scale, and .72 for the activity scale. The elated group (M = 5.57) reported more positive affect than the neutral (M = 4.44) and depressed (M = 3.40) groups, F(2,109) = 69.48, p < .OOl. Similarly, the elated group (M = 4.48) reported more activity than the neutral (M = 3.37) and depressed (M = 2.24) groups, F(2,109) = 28.12, p < .OOl. Least significant difference (LSD) tests indicated that the three mood groups were significantly different from one another on both affect and activity at the p < .05 level.5 4 The results reported below did not differ from those with suspicious subjects included. 5 The affect and activity items can be combined to create an internally consistent global mood scale (Cronbach’s a = .86). All mood groups differed from one another on this scale, as well. Some scale anchors were selected from the Multiple Affect Adjective Check List (MAACL; Zuckerman, Lubin & Rinck, 1983); and others were created based on face validity in order to represent both the affect and activity factors associated with mood states

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Dependent Measures Evaluation effects Halo error. Interdimension correlations among the eight behavioral categories were computed for the three mood groups and for the two order of information acquisition groups. These correlations for the three mood groups are presented in Table 1. Since subjects in positive affective states should be more likely to assimilate different information into the sameperformance category, that is, be more broad categorizers, and since subjects in negative affective states should make more discrete categorizations, it was predicted that greater halo should exist for subjects in positive and neutral states when compared to those in negative states, resulting in higher interdimension correlations. Neutral states were predicted to result in levels of halo falling between the levels of the elated and depressed groups. This prediction was confirmed. As is illustrated in the table, subjects in the elation condition displayed greater halo than did subjects in the depression condition. Twenty-five of the 28 correlations were smaller in magnitude for the depressed subjects relative to the elated group. The binomial probability that this effect occurred by chance is less than .OOl. Twenty-six of the correlations were smaller for subjects in the depressed group relative to the neutral group. The binomial probability that this effect occurred by chance is less than .OOl.Only 14 of the correlations were smaller for the neutral group, relative to the elated group. The mean interdimension correlations were .52, .50, and .36 for the elated, neutral, and depressed groups, respectively. An analysis of variance (ANOVA) testing the effect of mood on the interdimension correlations was significant, F(2,81) = 7.74, p < .OO1.6LSD tests indicated that the depressed group had a significantly lower mean interdimension correlation than the other (Russell, 1980; Sinclair & Thayer, 1986). Sinclair, Metzger, and Borkovec (1986) used the MAACL as a manipulation check for a mood induction like the one reported here. In Sinclair and Mark (1986a, April, 1986b), as well as in the present study, it was decided to use a shorter manipulation check (the instrument described in this paper). The effect magnitudes on the MAACL manipulation checks in Sinclair et al. (1986) were eta2’s of 26.97, 56.42, 51.36, 60.62, and 55.21 for anxiety, depression, hostility, positive affect, and sensation seeking, respectively. The eta2’s for the manipulation check in the present study were 61.50, 42.15, and 57.87 for the affect, activity and the global mood scales, respectively. Thus, the present scale, while much shorter than the widely used MAACL, appears to be as adequate a manipulation check instrument (especially given the magnitude of mood change associated with this induction). It is quick, internally consistent, and yields effect sizes apparently comparable to the MAACL. It should be noted that the neutral group from this study was not used in the comparison with the MAACL since Sinclair er al. (1986) had no neutral control. 6 The 28 interdimension correlations for each of the three mood groups were treated as individual data points for this analysis.

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MOOD AND PERFORMANCE APPRAISAL TABLE 1 INTERDIMENSIONCORRELATIONSAS A FUNCTION OF MOOD STATE Behavioral category AQ

DL

PO

AV

SN

FR

SE

GI

Mood

AQ

.59 .52 .39

.58 .54 .47

.57 .45 .36

.35 .36 .27

.56 .05 .31

.71 .41 .21

.44 .31 .28

Elation Neutral Depression

DL

*

.64 .84 .72

.39 .77 .54

.31 .64 .14

.32 .33 .16

.47 .46 .42

.70 .78 .72

Elation Neutral Depression

*

.63 .76 .44

.37 .60 .36

.50 .33 .27

.52 .44 .23

.72 .65 .58

Elation Neutral Depression

*

.59 .72 .31

.50 .36 .53

.68 .50 .40

3% .67 .56

Elation Neutral Depression

*

.56 .42 .47

.43 .36 .07

.41 .61 .18

Elation Neutral Depression

*

.54 .32 .21

.26 .32 .22

Elation Neutral Depression

.51 .58 .36 *

Elation Neutral Depression

PO

AV

SN

FR

SE GI

*

Note. AQ = answering questions; DL = delivering lectures; PO = preparation and organization; AV = availability; SN = sensitivity; FR = friendliness; SE = setting expectations; GI = generating student involvement.

groups, at the p < .05 level. Order of information acquisition had no apparent effect on halo error. Fourteen of the 28 correlations were of greater magnitude for the positive information first group. Since elated subjects display greater halo (broader categorization breadth) than do depressed subjects, it seems likely that regressing the specific behavioral measureson the global evaluation in a stepwise manner would result in a greater number of entries into the equation for depressed subjects relative to elated subjects (the global measure was the mean of the four global ratings; a = .88). Further, it is likely that greater predictability would result for the depressed group since there is less

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covariation among the specific behavioral measures. These effects would provide more evidence that subjects in depressed states are making more discrete categorizations, and that their global impressions are related to a greater number of specific behavioral judgments than are the impressions of elated subjects. For subjects in the elated condition, only the delivering lectures measure entered into the regression, R* = .23. For the neutral subjects, delivering lectures, followed by sensitivity entered, R* = .41. For the depressed group, availability entered, followed by delivering lectures, R* = .63. Thus, relative to the elated group, more diverse information contributes to the global evaluations of depressed subjects. Greater predictability also results.’ The neutral group fell between the other two groups. Accuracy. A within subject correlation between the number of positive behaviors in a particular category and the subject’s ratings of the teacher on that category was computed for each subject. This correlation served as the accuracy measure. * Subjects in the depression condition, as predicted, seem to be more accurate than subjects in other conditions. The mean within subject correlation was .37 for the depressed subjects, whereas it was .22 and .29 for the neutral and elated groups, respectively. A directional test of the effect of mood on accuracy was significant, F(2,109) = 2.43, p < .05 (the accuracy variable was transformed to normality).’ There were no effects of order of information acquisition. Mood and order did not interact. Global evaluations. Cronbach’s estimate of internal consistency was computed on the four global items with an estimate of .88; thus, the mean of the four items formed one global evaluation score. The mean of two judges’ 5-point scale ratings, anchored at 1 (extremely negative) and 5 (extremely positive), of the valence of the open-ended description of the teacher was the second measure of the subjects’ global evaluation of the target (inter-judge r = .65, p < .Ol; the judges were blind to conditions and made their ratings independently). These two measures were subjected to a multivariate analysis of variance (MANOVA) with mood state and order of information acquisition serving as the independent variables. The multivariate effect of mood was significant, F(4,212) = 2.95, p < .Ol. Subjects in the elation condition (M = 4.18) evaluated the teacher more positively than subjects in either the neutral (M = 3.52) or depression (M = 3.43) conditions, univariate F(2,109) = 5.74, p < .Ol. LSD tests indi’ Results using a forced entry procedure confirm the results of the stepwise procedure. The R2’s were .29, .49, and .66 for the elated, neutral, and depressed groups, respectively. * I thank Steve Motowidlo for suggesting this measure. 9 A post hoc analysis using the independent group’s pilot test ratings of the valence of the behaviors as the predictor in the accuracy measure revealed similar effects, directional F(2,109) = 2.64, p < .05.

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cated that the elation mean differed from both of the other means, which did not differ from one another. The same pattern was present in the judges’ evaluations. Subjects’ descriptions were more positive in the elation condition (M = 3.05) than in either of the neutral (M = 2.51) or depression (M = 2.46) conditions, F(2,109) = 4.59, p < .02. LSD tests, again, indicated that the elation mean differed from both of the other means, but that the neutral and depression means did not differ. The multivariate effect for order was also significant, F(2,107) = 12.47, p < .Ol. Subjects encoding initial positive information made more positive global evaluations (M = 4.21) than those encoding initial negative information (M = 3.21), univariate F(1,109) = 24.29, p < .Ol. This was also true of the judges’ evaluations. Subjects who encoded initial positive information, wrote more positive descriptions of the target (M = 2.98) than did subjects who encoded initial negative information (M = 2.36), univariate F( 1,109) = 11.25, p < .Ol . The latter 2 effects reflect primacy. The multivariate mood x order interaction was nonsignificant, F(2,412) = 0.07, Its. Memory Effects (Information

Retrieval)

Two judges, who were blind to conditions, independently counted the number of positive and negative behaviors retrieved by the subjects. The interjudge reliabilities were .91 and .82 for the number of positive and negative behaviors recalled, respectively. Thus, the means of judges’ counts were used for these analyses. A mixed model ANOVA was conducted with mood state and order of information acquisition as between subject factors and valence of information retrieved as a within-subject factor. There was a marginal main effect for order, F(1,109) = 2.97, p < .09, and a main effect for valence of information retrieved, F(1,109) = 5.64, p < .02. Both of these effects had to be interpreted in the context of two higher order interactions. There was a mood x valence of information retrieved interaction on the amount of information retrieved, with the pattern suggesting that subjects in the elated group retrieved more positive and less negative information when compared to subjects in the neutral and depressed groups, F(2,109) = 4.23, p < .02. This effect is illustrated in Fig. 1. Subjects in depressed and neutral states retrieved less positive information than did subjects in elated states. Subjects in both depressed and neutral states retrieved less positive than negative information. The depressed and neutral groups did not differ in retrieval of positive or negative information. The neutral group retrieved less positive information than the elated group retrieved negative information. LSD tests indicated that each of these effects was significant at the p < .05 level. The elated group, as predicted, retrieved more positive than nega-

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b

h a v i 0 r s

5.6

1

5.1 50

+/-

,/’ /

4.9 / 46

R e 47

/

0’ Elation Neutral Depression

-I

o-----o +-

-+

Negative

Positive VALENCE

OF INFORMATION

RETRIEVED

FIG. 1. Number of behaviors retrieved as a function of mood state and information valence.

tive information at the p < .ll level. These results are generally consistent with mood congruency effects. There was also an order x valence of information retrieved interaction on amount of information retrieved, F(1,109) = 15.72,p < .Ol. The order groups differed in the amount of positive information retrieved, with the initial positive information group retrieving more positive information (A4 = 5.85) than the initial negative group (A4 = 4.51). However, no differences were seen with respect to negative information retrieved between groups (A4 = 5.54 and M = 5.82 for the initial positive and initial negative groups, respectively). Subjects encoding initial negative information retrieved more negative information (M = 5.82) than they did positive (A4 = 4.51). Finally, subjects encoding initial positive information did not differ with respect to the amount of positive or negative information re-

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trieved, although the direction of the effect is consistent with a primacy effect. LSD tests indicated that the differences reported here were significant at the p < .05 level. Thus, the type of information retrieved seems to reflect a primacy effect based on order of information acquisition. There were no mood x order or mood x order x valence of information retrieved interactions. DISCUSSION

Consistent with the literature on categorization breadth and affective state (Sinclair & Mark, 1986a,April, 1986b;O’Malley & Davies, 1984;cf. Isen & Daubman, 1984), subjects in depressed moods displayed the least halo and greatest accuracy in their performance appraisaljudgments. This increased accuracy in depressed subjects is consistent with literature in the area of chronic depression, accuracy of attributions, and the perception of control (Abramson, Alloy, & Rosoff, 1981; Alloy & Abramson, 1979; Alloy & Abramson, 1982; Alloy, Abramson, & Viscusi, 1981; Kuiper, 1978; Ruehlman, West, & Pasahow, 1985). Depressed subjects may be processing information in a more controlled manner than are elated subjects (Clark & Isen, 1982; Sinclair & Mark, 1986a,April). This control may result in more discrete judgments, less halo, greater accuracy, and more substantial relationships between global evaluations and specific behavioral ratings. Fisk and Schneider (1984) demonstrated that subjects in controlled processing modes recall distractor items; whereas, subjects in automatic modes do not. Subjects in automatic modes may not encode the distractor items. If subjects in elated states are in automatic processing modes and form quick initial impressions, it may be the case that subsequent impression-inconsistent information is seen as distracting, and hence not encoded. Thus, like the distracters in the Fisk and Schneider study, this type of information would have no effect. Isen and Means (1983) (see also Isen, 1984) have shown that subjects in elated states take less time to make decisions, review information less, and display differential processing strategies when compared to a control group (see Clark & Isen, 1982,and Isen, 1984,for discussion of differential processing strategies). Although Isen and Means found no differences in the decisions made, this result is not incompatible with the results of the present study. Task differences may account for the differential effects. It seems likely that deciding on promotion, tenure, and pay increases may be a relatively complex task when compared to a laboratory automobile purchase task (the Isen 8z Means, 1983, task). In the present study, subjects were led to believe that their input would have ramifications in the type of strategy used in evaluating and promoting professors. Automatic processing and broad categorization may lead to the same

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decision as a controlled processing strategy for simple tasks; different decisions may result when complex tasks are encountered. The degree to which previous results have been replicated should be addressed. In the present study, in O’Malley and Davies (1984; as reinterpreted by Sinclair & Mark, 1986a, April), and in Isen and Daubman (1984) subjects in positive moods categorized most broadly. O’Malley and Davies found positive-neutral differences, but they also found more profound positive-negative differences. Here positive-negative differences, but no positive-neutral differences were found; although the ordering of effects was the same as those found by O’Malley and Davies. Isen and Daubman (1984) found only positive-neutral differences and an order of effects inconsistent with O’Malley and Davies, and the present effects (as well as inconsistent with those of Sinclair & Mark, 1986a, April, 1986b, whose pattern of effects were similar to the present effects and those of O’Malley & Davies). Isen and Daubman found a positive, negative, neutral effect order. The inconsistency in the order of effects (the present effects and those of O’Malley & Davies versus Isen & Daubman) may be due to either of two factors. First, the type of mood induction used varies across the three studies. Thus, it is unclear that the moods induced in the three studies are identical. From the vantage point of external validity, it is useful to note that in all cases subjects in positive moods tended to be the broadest categorizors regardless of the mood induction used. Second, the nature of the stimuli to be judged varied across the studies. Both the present study and that of O’Malley and Davies used social stimuli having affective implications (although this study used stimuli that were otherrelated, whereas O’Malley & Davies’ stimuli were self-related). Isen and Daubman used more cognitive-nonsocial stimuli that are normally considered affectively neutral. This difference in stimuli is important since Mark and Miller (1986) have shown that categorization breadth on nonsocial tasks is unrelated to breadth in reward allocation (a social task); whereas, breadth on social tasks is related to reward allocation, especially when the category is one directly related to performance levels. These differences may explain the minor inconsistencies across studies. However, there is an important consistency across all studies that should not overshadow the few inconsistencies; subjects in positive moods tended to be the broadest categorizors. Finally, the substantial differences in R2's, across the mood groups, for predicting global evaluations from discrete judgments in this study were consistent with O’Malley and Davies (1984). Elated subjects categorized most broadly, neutral subjects a moderate amount, and depressed subjects the least. Thus, more diverse information contributed to the global impressions formed by the depressed (R2 = .63), less for neutral subjects (R2 = .41), and least for elated subjects (R2 = .23). The average interdimension correlations (halo) were in the same

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direction also. The effect order was the same as in the O’Malley and Davies study; that is, positive moods led to more broad categorization than neutral moods, and neutral to more broad categorization than negative moods. Thus, the present results, those of O’Malley and Davies, and those of Isen and Daubman are consistent regarding the broad categorization associated with elated subjects. More research must be conducted to investigate the role of affective state in organizational decisions. However, the present study does demonstrate that affective state has potential effects in performance appraisal decisions. Feeling states can influence our encoding and retrieval of information about a target, and contribute to the type of information processing strategy used by the decision maker. Although positive states may result in quicker decisions (Isen & Means, 1983), they may result in greater halo and less accuracy, relative to negative states. This point is worth expanding: Although positive states bias information retrieval and evaluations in a manner consistent with the state, this may not be true of negative states. Instead, depressed states may lead to a processing strategy that results in less error. Thus, the same underlying process that results in less differentiation, greater halo, and less accuracy for subjects in positive affective states (broad categorization or automatic processing) leads to greater differentiation, less halo, and greater accuracy for subjects in depressed affective states (narrow categorization or controlled processing). The common core is the nature of categorization breadth. Elated subjects categorize broadly and form sweeping global impressions. Depressed subjects categorize more narrowly, may assess more facts, and make more discrete judgments resulting in less error. This error reduction in depressed subjects may be related to more algorithmic processing and less heuristic processing relative to elated subjects (Isen, Means, Patrick, & Nowicki, 1982).Use of heuristics can increase error in complex judgments. Thus, use of heuristics may increase halo and partially account for more global categorizations. The results of this study suggest that heuristic use on the part of elated subjects is a potential explanation for the broad categorization associated with positive states (see Isen & Daubman, 1984). Subjects in depressed states may be processing information in more controlled or less heuristic manners resulting in reduced halo, greater accuracy, and greater predictability of global evaluations from specific ratings. Since error reduction and increased accuracy are the goals of performance appraisal, it seems that being in a depressed state when conducting appraisals may not necessarily be undesirable. This seemsparadoxical since performance appraisers are often advised to “get into a good mood” prior to the rating task, thus avoiding the potential problem of inordinately low ratings (see Longenecker, 1984). Yet, as Longenecker (1984) describes, raters generally report depressed

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affect at the thought of conducting evaluations. The results of the present study would suggest that the depressed state may not be detrimental to performance appraisaljudgments. Ideally, to reduce the potential biasing effects of mood on performance appraisal judgments, it may be reasonable to conduct numerous appraisals and use the aggregate scores in yearly performance reviews. lo Further, the results suggest that training programs for evaluators could focus on learning narrow categorizationlearning to discriminate among cognitive stimuli (cf. Isen & Daubman, 1984~the narrow categorization training could later be generalized to person-related stimuli (e.g., performance appraisal, selection interviews, applications, etc.). Care should be taken in generalizing the potentially beneficial effects of negative mood states outside of the performance appraisal area. While it may be true that negative moods enhance the validity of performance appraisal judgments, negative moods may have deleterious effects in other areas. For example, negative mood states during feedback could make an inordinate amount of negative information accessible leading to unjustifiably negative performance feedback (like the main effect for mood on retrieval and global evaluations in the present study). This, in turn, could lead to supervisor-subordinate conflict (Larson, 1984). Further, bad moods may generate a tendency to combine emotional and behavioral feedback making it difficult for the high performer on a complex job to draw clear conclusions concerning exactly what is being evaluated and rewarded. lo The effects of order of information acquisition suggestthat randomizing notes on critical incidents regarding employees, prior to evaluation, may enhance the validity of the evaluation process. Finally, supervisors should actively seek examples of both positive and negative work-related behaviors. This may aid in reducing the effects of both primacy and mood congruency that could decrease the validity of evaluations. Mood state also affected the general valence of ratings and written descriptions; subjects in positive states evaluated and described the target more positively than those in neutral or negative states. Also, mood consistent (congruent) information was more likely to be retrieved from memory than was mood inconsistent information, for the elated and depressed groups. These results are reasonably consistent with Isen et al. (1978), Teasdale and Fogarty (1979), Bower (1981), Snyder and White (1982), and Teasdale and Russell (1983) who found that mood state biases retrieval. Mood seemsto activate associated cog&ions and enhance their accessibility (Isen et al., 1978).” lo I thank two anonymous reviewers for these suggestions. ‘I The pattern of retrieval effects reported here fail between the “accessibility”

effects

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In the present study, neutral subjects retrieved more negative than positive information. This effect is not suprising, in that Jones and Davis (1965), Hamilton and Zanna (1972), and Wyer and Gordon (1982) have suggestedthat negative information is weighted more heavily, and is more easily retrieved, in impression formation. Thus, subjects in neutral moods are retrieving information that is more heavily weighted in the forming of their impressions. The information retrieved in the present study differs from that generally used in that other-oriented information was being retrieved, rather than self-oriented information or affectively laden word lists (cf. Isen et al., 1978; Snyder & White, 1982). This difference in stimuli may account for the neutral subjects weighting and retrieving more negative than positive information. Alternatively, it could be argued that the negative information was seen as more important than the positive in the present study. This may be true when impressions are being formed (cf. Hamilton & Zanna, 1972;Jones & Davis, 1965);however, the individual negative behaviors were seenas slightly less important than the positive behaviors at baseline (see Footnote 2). Finally, subjects in the neutral group in the present study may have been in slightly negative moods relative to those in other research. Retrieval bias may influence global impressions somewhat like accessibility at output (Anderson & Pichert, 1978;Higgins & King, 1981; Higgins & Rholes, 1978).Future research could address the causal direction of mood, retrieval, and impression formation by varying mood state as well as the order of dependent measures. Some subjects would complete the retrieval task prior to evaluations, whereas others would complete the task in reverse order (cf. Hirt & Sherman, 1986). That is, does making judgments affect retrieval? Does retrieving differential information affect judgments? And, do these interact with mood? The valence of information initially encoded affected judgments and reported by Isen et al. (1978) and the “mood congruency” effects reported by Bower (1981). Isen et al. found asymmetric retrieval effects in that subjects who succeeded at a task appear to be retrieving more positive words from a learned word list than did subjects who failed. There appeared to be no difference in the number of neutral or negative words retrieved between the two groups. There was a fan-spread pattern in the Isen et al. results, but no cross-over interaction. Further comparisons to the present study are difficult since Isen et al. did not report follow-up tests indicating which means differed. Bower found a symmetric cross-over interaction; subjects in good moods retrieved more positive and less negative information than subjects in sad moods. In the present case, as in Bower (1981), there was a cross-over interaction (unlike Isen et al., 1978)and as in Isen et al., there was no difference between the amount of negative information retrieved between subjects in good versus bad moods. Part of this slight inconsistency across the three studies may be due to the inability to compare the degree of negativity of the stimuli used in each. Regardless, mood state at retrieval seems to act as an accessibility at output manipulation (Anderson & Pichert, 1978; Higgins & Rholes, 1978; Isen et al., 1978).

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retrieval. Subjects encoding initial positive information evaluated the target more positively and retrieved more positive information relative to those who encoded initial negative information. This is yet another example of primacy effects resulting from encoding bias in impression formation. It is likely that impressions are formed quickly, based on initial information. Subsequent information, if consistent with the impression, is assimilated into the impression; inconsistent information is rejected, or attributed to something other than the internal characteristics of the target being judged (Larson, 1984; Schneider, Hastorf, & Ellsworth, 1979). Once impressions are formed, facts seem to be forgotten, and labels persevere. This results in more polarized and internally consistent impressions, and in the inability to retrieve much behavioral information. Information retrieval becomes further contaminated with reconstruction errors consistent with the impression (Higgins et al., 1977; Higgins & King, 1981; Sinclair eT al., 1987). Finally, the results of this study suggest that encoding and retrieval biases act independently in the performance appraisal judgments described here. Mood state and order of information acquisition did not interact. Performance appraisal decisions, like other cognitive processes, can become contaminated at both the encoding and retrieval stages; yet, these types of contamination did not affect one another in this study. That is, in this study there were no encoding specificity effects. Construct accessibility at encoding or input (as operationalized by the order of information acquisition manipulation) affected subsequent impression formation and information retrieval. Accessibility at retrieval or output (as operationalized by the mood manipulation) affected impression formation and information retrieval. The lack of interaction between the input and output manipulations could suggest that accessibility at input and at output act independently in person perception. Further research examining this hypothesis should vary the time at which the impression formation and retrieval measures are taken, since accessibility at input effects have been shown to become more profound over time (Higgins et al., 1977; Sinclair et al., 1987). It may be useful to consider the effects of different positive and negative states on performance judgments (cf. Russell, 1980; Sinclair & Thayer, 1986; Thayer & Sinclair, 1985; Thayer & Sinclair, 1986, April). Srinivas and Motowidlo (1985) have found that a stress manipulation increases halo error relative to a control group. The stress manipulation was correlated with the general dysphoria scale of the MAACL (Zuckerman & Lubin, 1965). Thus, one negative affective state resulting from stress leads to greater halo, whereas depression leads to less halo. Srinivas and Motowidlo argue that stress is aversive and arousing. The arousing nature of the state may lead to automatic processing and greater halo,

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much like misattribution and excitation transfer effects (Schachter & Singer, 1962; Zillmann, Johnson, & Day, 1974). Depression, on the other hand, is associated with low levels of arousal leading to more controlled processing modes, less halo, and greater accuracy. The effects of affective state on categorization judgments may depend on both state valence and state arousal. However, this argument may be somewhat utopian in that finer gradations of affective state can always be made (i.e., negative active states can be broken down into anxiety versus anger), leading to an endless variety of potential mood effects. In the present study narrow categorization resulting from depression led to more discrete judgments, less halo, greater accuracy, and greater predictability of global evaluations from specific ratings. However, stimulus characteristics may play an important role in these effects. Varying the degree to which behavioral information about a target is correlated in reality should interact with mood state, affecting halo and accuracy. For example, highly intercorrelated behaviors should not lead to differential halo for depressed versus elated states. Moderately intercorrelated behaviors should result in slightly greater halo for subjects in elated states. Behavioral information that is relatively uncorrelated should lead to profound differences in halo between subjects in elated versus depressed states (given the definition of halo used in the present study). That is, the validity of predictions and effects found in this study may depend on both the correlations that exist within the actual performance distributions and categorization breadth. Thus, one key issue to be addressed in future research is the congruence or equality of categorization breadths between the actual performance distribution and the cognitive processes of the evaluator. Similar effects should be seen with the accuracy measure. Accuracy and halo could be a function of the congruence of categorization breadths rather than only the breadths in the evaluator’s cognitions. Thus, automatic and controlled processing modes and categorization breadth differences in the evaluator should only lead to differential judgments when the target information is not truly highly correlated. This argument is sensible if negative mood states result in narrower categorizations through more discrete judgments and reduced heuristic use. However, if the narrow categorizations seen in those in depressed states are a result of a propensity to categorize narrowly, without concern for the true nature of the stimuli, then narrow categorization could result in error when stimuli are highly related and broad categorization is called for. The results of the present study suggest that research addressing performance appraisal may be fruitfully pursued through the conceptualization of the performance appraisal judgment as a categorization task. Mood state seems to affect judgments in a manner consistent with a categorization breadth interpretation. Future research should address the

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external validity of the present study in organizational settings, since applying the categorization breadth effects discussed here without such validation may be inappropriate. REFERENCES Abramson, L. Y., Alloy, L. B., & Rosoff, R. (1981). Depression and the generation of complex hypotheses in the judgment of contingency. Behaviour Research and Therapy, 19, 35-45. Alloy, L. B., & Abramson, L. Y. (1979). Judgment of contingency in depressed and nondepressed students: Sadder but wiser? Journal of Experimental Psychology: General, m&441-485. Alloy, L. B., & Abramson, L. Y. (1982). Learned helplessness, depression, and the ihusion of control. Journal of Personality and Social Psychology, 42, 1114-l 126. Alloy, L. D., Abramson, L. Y., & Viscusi, D. (1981). Induced mood and the illusion of control. Journal of Personality and Social Psychology, 41, 1129-l 140. Anderson, R. C., & Pichert, J. W. (1978). Recall of previously unrecallable information following a shift in perspective. Journal of Verbal Learning and Verbal Behavior, 17, l-12. Asch, S. E. (1946). Forming impressions of personality. Journal of Abnormal and Social Psychology, 41, 258-290. Borkovec, T. D., Robinson, E., Pruzinsky, T., & Depree, J. A. (1983). Preliminary exploration of worry: Some characteristics and processes. Behavior Research and Therapy, 21, 9-16. Bower, G. (1981). Mood and memory. American Psychologist, 36, 129-148. Clark, M. S., & Fiske, S. T. (Eds.). (1982). Affect and cognition. Hillsdale, NJ: Erlbaum. Clark, M. S., & Isen, A. M. (1982). Toward understanding the relationship between feeling states and social behavior. In A. H. Hastorf & A. M. Isen (Eds.), Cognitive social psychology (pp. 73-108). New York: Elsevier. Cooper, W. H. (1981). Ubiquitous halo. Psychological Bulletin, 90, 218-244. Farr, J. L. (1973). Response requirement and primacy-recency effects in a simulated selection interview. Journal of Applied Psychology, 57, 228-232. Farr, J. L., & York, C. M. (1975). Amount of information and primacy-recency effects in recruitment decisions. Personnel Psychology, 28, 233-238. Fisk, A. D., & Schneider, W. (1984). Memory as a function of attention, level of processing, and automatization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 181-197. Flexser, A. J., & Tulving, E. (1978). Retrieval independence in recognition and recall. Psychological Review, 85, 153-171. Hamilton, D. L., & Zanna, M. P. (1972). Differential weighting of favorable and unfavorable attributes in impressions of personality. Journal of Experimental Research in Personality, 6, 204-212. Harari, O., & Zedeck, S. (1973). Development of behaviorally anchored rating scales for the evaluation of faculty teaching. Journal of Applied Psychology, 58, 261-265. Higgins, E. T., & Rholes, W. (1978). “Saying is believing”: Effects of message modification on memory and liking for the person described. Journal of Experimenral Social Psychology, 14, 363-378. Higgins, E. T., &Ring, G. (1981). Accessibility of social constructs: Information processing consequences of individual and contextual variables. In N. Cantor & J. F. Kihlstrom (Eds.), Personality, cognition, and social interaction (pp. 69-122). Hillsdale, NJ: Erlbaum. Higgins, E. T., Rholes, W. R., &Jones, C. R. (1977). Category accessibility and impression formation. Journal of Experimental Social Psychology, 13, 141-154.

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Hirt, E. R., Bt Sherman, S. J. (19%). The role ofprior knowledge in explaining hypothetical events. Journal of Experimental Social Psychology, 21, 519-543. Isen, A. M. (1984). Toward understanding the role of alTect in cognition. ln R. S. Wyer & T. K. Srull (Eds.), Handbook of social cognition, Vol. 3 (pp. 174-236). Hillsdale, NJ: Erlbaum. Isen, A. M., & Daubman, K. A. (1984). The influence of affect on categorization. Journal of Personality and Social Psychology, 47, 1207-1217. Isen, A. M., & Means, B. (1983). The influence of positive affect on decision making strategy. Social Cognition, 2, 18-31. Isen, A. M., Means, B., Patrick, R., & Nowicki, G. (1982). Some factors intluencing decision-making and risk taking. In M. S. Clark L S. T. Fiske (Eds.), Aflect and cognirion: The 17th annual Carnegie Symposium on Cognition (pp. 243-261). Hillsdale, NJ: Erlbaum. Isen, A. M., Shalker, T., Clark, M., & Karp, L. (1978). Affect, accessibility of material in memory, and behavior: A cognitive loop? Journal of Personality and Social Psychology, 36, l-12. Johnson, E., & Tversky, A. (1983). Affect, generalization, and the perception of risk. Journal of Personality and Social Psychology, 45, 20-31. Jones, E. E., & Davis, K. E. (1%5). From acts to dispositions. In L. Berkowitz (Ed.), Advances in experimental social psychology, Vol. 2 (pp. 219-266). New York: Academic Press. Jones, E. E., & Goethals, G. R. (1972). Order effects in impression formation: Attribution context and the nature of the entity. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. E. Nisbett, S. Valins, & B. Weiner (Eds.), Attribution: Perceiving the causes of behavior (pp. 274). Morristown, NJ: General Learning Press. Jones, E. E., Goethals, G. R., Kennington, G. E., & Severence, L. J. (1972). Primacy and assimilation in the attribution process: The stable entity proposition. Journal of Personality, 40, 250-274. Kuiper, N. A. (1978). Depression and causal attributions for success and failure. Journal of Personality and Social Psychology, 36, 236246. Larson, J. R. (1984). The performance feedback process: A preliminary model. Organizational Behavior and Human Performance, 33, 42-76. Longenecker, C. 0. (1984). Executive cognition and affect in performance appraisal. Unpublished dissertation, The Pennsylvania State University. Luchins, A. S. (1957). Primacy-recency in impression formation. In C. Hovland (Ed.), The order of presentation in persuasion (pp. 33-61). New Haven, CT: Yale University Press. Mark, M. M., L Miller, M. L. (1986). Categorization of social, non-social, and performance-related stimuli. Manuscript in preparation, The Pennsylvania State University. O’Malley, M. N., & Davies, D. K. (1984). Equity and affect: The effects of relative performance and moods on resource allocation. Basic and Applied Social Psychology, 5, 273-282. Ruehlman, L. S., West, S. G., & Pasahow, R. J. (1985). Depression and evaluative schemata. Journal of Personality, 53,46-91, Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-l 178. Sal, F. E., Downey, R. G., & Lahey, M. A. (1980). Rating the ratings: Assessing the psychometric quality of rating data. Psychological Bulletin, 88, 413-428. Schachter, S., C Singer, J. E. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69, 379-399. Schneider, D. J., Hastorf, A. H., & Ellsworth, P. C. (1979). Person perception (2nd ed.). Reading, MA: Addison-Wesley.

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Sinclair, R. C., Hendrickson, C., & Motowidlo, S. J. (1985). [Stress and automatic versus controlled processing in performance appraisal judgements]. Unpublished raw data, Central Michigan University. Sinclair, R. C., & Mark, M. M. (1985). Mood induction and the proper unit ofanalysis: A validation of a mass administration of the Velten Mood Induction Procedure. Unpublished manuscript, Central Michigan University. Sinclair, R. C., & Mark, M. M. (1986a, April). Mood andjustice judgments: A categorization breadth interpretation. Paper presented at the Annual Conference of the Eastern Psychological Association, New York. Sinclair, R. C., & Mark, M. M. (1986b). Mood, categorization breadth, and justice orientations. Manuscript in preparation, Central Michigan University. Sinclair, R. C., Mark, M. M., & Shotland, R. L. (1987). Construct accessibility and generalizability across response categories. Personality and Social Psychology Bulletin, 13, 239-252. Sinclair, R. C., Metzger, R., & Borkovec, T. D. (1986). Mood schema, demand characteristics, and response time: The effect of mood state on response time to affectively laden stimuli. Manuscript in preparation, Central Michigan University. Sinclair, R. C., & Thayer, J. F. (1986). The structure of affective states and the use of the Multiple Affective Adjective Checklist: Evidence for orthogonal bipolar affect and activity dimensions. Manuscript in preparation, Central Michigan University.

Snyder, M., & White, P. (1982). Mood and memories: Elation, depression, and the remembering of one’s life. Journal of Personality, 50, 14sl67. Springbett, B. M. (1958). Factors affecting the final decision in the employment interview. Canadian Journal

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Srinivas, S., & Motowidlo, S. J. (1985). Effects of stress on information processing in performance evaluation. Unpublished manuscript, The Pennsylvania State University. Teasdale, J. D., & Fogarty, S. J. (1979). Differential effects of induced mood on retrieval of pleasant and unpleasant events from episodic memory. Journal of Abnormal Psychology, 88, 248-257. Teasdale, J. D., & Russell, M. L. (1983). Differential effects of induced mood on the recall of positive, negative, and neutral words. British Journal of Clinical Psychology, 22, 163-172. Thayer, J. F., & Sinclair, R. C. (1985). A comment on Gotlib and Meyer: Are positive and negative affective states really orthogonal? Unpublished manuscript, The Pennsylvania State University. Thayer, J. F., & Sinclair, R. C. (1986, April). Further evidence of a heirarchical model of psychological distress. Paper presented at The Annual Conference of the Eastern Psychological Association, New York. Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic memory. Psychological Review, 80, 352-373. Velten, E. A. (1968). A laboratory task for induction of mood states. Behavior Research and Therapy, 6, 473482.

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Zillmann, D., Johnson, R. C., & Day, K. D. (1974). Attribution of apparent arousal and proficiency of recovery from sympathetic activation affecting excitation transfer to aggressive behavior. Journal of Experimental Social Psychology, 10, 503-515. Zuckerman, M., & Lubin, B. (1965). Manual for the Multiple Affect Adjective Check List. San Diego, CA: Educational and Industrial Testing Service. Zuckerman, M., Lubin, B., & Rinck, C. M. (1983). Construction of new scales for the Multiple Affect Adjective Check List. Journal of Behavioral Assessment, 5, 119-129. RECEIVED:

February 20, 1986