Decision bias and personnel selection strategies

ORGANIZATIONAL

BEHAVIOR

AND

HUMAN

DECISION

PROCESSES

4.0,

136- 147(1987)

Decision Bias and Personnel Selection Strategies VANDRA L. HUBER Department

of Management,

University

of Utah

AND

MARGARET A. NEALEANDGREGORY Department

of Management

and Policy,

B. NORTHCRAFT

University

of Arizona

The purpose of this study was to examine the influence of two decisional biases-framing and cost salience-on personnel selection decisions. One hundred twenty-eight graduate and undergraduate students participated in a personnel selection simulation. Framing was manipulated by inducing participants to use either a “rejecting” strategy (identify those applicants whom you would not interview) or an “accepting” strategy (list those applicants whom you would interview). Cost salience was manipulated by making selection-related costs either implicit or explicit. Results showed that “accepting” strategy subjects selected less applicants to be interviewed than “rejecting” strategy subjects, but only when selection-related costs were made salient. More time was required for subjects to make their selection decisions when selection-related costs were made salient. Framing and cost salience also influenced the success probability thresholds used by subjects to select apphcants. Limitations of this research and directions for future study were discussed. 0 1987 Academic Press, Inc.

Selection is the set of procedures through which an organization chooses its human resources. Selection obviously is an important and consequential activity. Studies suggest that productivity differences between poor and superior performers in an organization can amount to more than $20,000 per employee per year (e.g., Schmidt & Hunter, 1981; Schmidt, Hunter, & Pearlman, 1982). Thus, an organization that chooses employees well can expect to reap substantial benefits. This paper examines the susceptibility of applicant selection choices to judgmental biases which may substantially erode the effectiveness of selection procedures. Selection can be characterized as a four-part process: (1) analysis of The authors thank Paulie Johnson and Steve Hanks for their assistance on the project, and two anonymous reviewers for the helpful comments on an earlier draft of the manuscript. Requests for reprints should be sent to Dr. Vandra Huber, Department of Management and Organization, University of Washington, Seattle, WA 98195. 136 0749-5978187$3.OO Copyright Q 1987 by Academic Press, Inc. All rights of reproduction in any form reserved.

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job requirements, (2) recruitment (i.e., the identification of job applicants), (3) testing and evaluation of applicants’ qualifications, and (4) deciding which applicants to hire. In practice, the last stage of this process often entails two steps: paring down the applicant pool to manageable size and then making a final hiring decision. The first three stages of this process-job analysis, recruitment, and testing-have been subject to considerable prescriptive and legislative attention. “Fair employment practices” legislation (such as the 1978 Uniform Guidelines on Employee Selection) regulates what must be done in recruiting and testing of job applicants. Further, there are a variety of standardized techniques available for job analysis (e.g., McCormick, 1976; 1979; van Rijn, 1979) and volumes have been written on statistical correctness and legal acceptability of applicant assessment techniques (e.g., Schmidt & Hunter, 1981; Schmidt et al., 1982; Schneider, 1976). Comparatively less attention has been devoted to how all the amassed selection information is or should be integrated to produce selection decisions. One long-standing stream of research in this area has explored the interplay of selection instrument validity and selection decision rules from the perspective of utility analysis concepts (Brogden, 1949; Cascio, 1980; Cronbach & Gleser, 1965; Landy, Farr, & Jacobs, 1982). Included have been studies examing the differential impact of various selection decision rules (e.g., Cronbach, Yalow, & Schaeffer, 1980; Ledvinka, 1979). In general these studies have analyzed the fourth component of the selection process-deciding which applicants to hire-by focusing on characteristics and implications of formal choice strategies. A more recently developing stream of research has provided new perspectives for understanding the choice component of selection decisions by focusing attention on the information-processing propensities of decision makers. Studies have shown that decision-making processes (such as selection choices) can be substantially biased by irrelevant cues or inappropriate information-processing strategies (for a review, see Kahneman, Slavic, & Tversky, 1982). One such source of decision bias is decisionframing (Tversky & Kahneman, 1973). Framing has to do with ways in which a decision maker’s risk propensities can be influenced by the presentation of choice alternatives. In a theoretical discussion of the framing bias, Kahneman and Tversky (1979) suggested that decision makers treat the prospect of gains differently than the prospect of losses. Decision makers are risk seeking (prefer riskier alternatives) when evaluating prospective losses, but risk averse (prefer certain outcomes) when evaluating prospective gains. Framing effects can be elicited by either features of the choice itself or features of the context in which the choice occurs. For instance, whether

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a decision maker is evaluating the prospect of gains or losses may be simply a matter of the way a choice is presented (e.g., “Is the pitcher half empty,” versus “Is the pitcher half full?“) Thus, the framing of a judgment, and thereby the risk preference exercised by the decision maker, may result from characteristics embedded in the task which are irrelevant to the task itself. On the other hand, framing effects also may be elicited by contextual features, such as the role assumed by a decision maker. For example, Neale, Huber, and Northcraft (1987) have shown that the labels “buyer” and “seller” differentially frame decision makers. In a series of studies, buyers proved more profitable than sellers, despite competing in a symmetrical market. This finding was explained by noting that buyers and sellers often find themselves exchanging goods of negotiable (i.e., indeterminant) value for explicitly valued money. Because the money is the salient source of value in the exchange, the attention of sellers apparently is more likely focused on the receipts gained from a sale (thereby eliciting risk aversion), while the attention of buyers is focused on the money being surrendered (i.e., lost), thereby eliciting riskseeking behavior. Differences in the risk propensities of buyers and sellers in turn lead them to adopt different negotiation strategies and thereby produce different outcomes (i.e., profitability). The framing bias may be particularly relevant to understanding selection decision making. Gordon (1986) has noted that once all relevant selection decision information has been collected, there are two basic strategies for integrating the information to pare down the pool of applicants. In the first strategy, selection criteria are identified through job analysis and then job applicants low on any of the selection criteria are rejected from further consideration. In the second strategy, the identified selection criteria are used to assign “suitability” points to each job applicant and then the higher scoring applicants are accepted for further consideration. While both strategies require an assessment of congruence between job requirements and applicant qualifications, the strategies differ in focusing the decision maker on rejecting versus accepting job applicants. Cognitively, this difference between the two strategies may result in differential framing of the selection decision maker as well. Accepting job applicants carries with it an implicit tone of gains (positive framing), and thereby should elicit risk aversion in decision makers. Rejecting applicants, on the other hand, has the flavor of losses (negative framing), and thus should induce risk-seeking behavior. Operationally, this should lead decision makers using an applicant “rejection” strategy to judge more applicants acceptable than decision makers using an applicant “acceptance” selection strategy. Thus, if the role of initial selection decisions is to pare

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an applicant pool down to a size manageable for a final decision-making process (such as personal interviewing), applicant “rejection” strategies should prove less efficient in narrowing down the applicant pool. Cognitive psychological research also suggests that generally it is more difficult and time-consuming to process negatively framed (compared to positively framed) information (Wason, 1959). Paradoxically, then, applicant “rejection” strategies for selection decision making not only should prove less efficient in narrowing initial applicant pools, but also should result in longer processing time to reach these decisions. A second factor to consider in the biasing of selection decisions is cost. In a variety of decision settings (e.g., Neale, 1984; Northcraft & Neale, 1986), increasing the salience of implicit costs of decision options has had dramatic effects on decision behavior. Increasing the salience of the implicit costs of decision options makes those costs more available (Tversky & Kahneman, 1973) to decision makers, thereby increasing the estimated probability that those costs will be incurred. The end result is a more conservative (less risky) attitude toward decision options when the implicit costs of those options have been made salient. In the same vein, increasing the salience of selection-related costs also should influence the amount of time a decision maker is willing to spend contemplating a choice. Research has shown (e.g., Connolly & Serre, 1984) that as decisions become more consequential, decision makers are willing to expend more resources (time, money, staff, etc.) making their choices. Because increasing the salience of selection-related costs highlights their consequential nature, increasing the salience of costs should increase decision processing time. Interestingly, while increasing the perceived importance of decisions increases processing time, it is not obvious that this increased processing time results in commensurate increases in decision quality (Oskamp, 1965). The importance of framing and cost salience to selection decisions was examined in the context of the laboratory selection simulation described below. Following from the above discussion, six hypotheses were proposed. Framing Hypotheses Hla. Applicant “rejection” selection strategies will negatively (loss) frame decision makers; applicant “acceptance” selection strategies will positively (gain) frame decision makers. As a consequence, negatively framed decision makers (“rejection” strategy) will judge more job applicants acceptable than the positively framed (“acceptance” strategy) decision makers. Hlb. Positively framed decision makers (“acceptance” strategies) will

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use a stricter decision criterion (higher, hence less risky, acceptance threshold) for selecting applicants for interviews. HI c. Selection decision strategies which negatively frame the decision makers (“rejection” strategies) will result in longer decision-processing time than decision strategies which positively frame (“acceptance” strategies) the decision maker. Cost Salience Hypotheses H2a. Increasing the salience of selection-related costs will make decision makers more conservative, leading decision makers to judge fewer job applicants acceptable than when costs are implicit. H2b. Increasing the salience of selection-related costs will raise the decision maker’s acceptance threshold (selection criterion) for selecting applicants for interviews. H2c. Increasing the salience of selection-related costs will increase the amount of decision-processing time necessary to evaluate the applicants. METHOD Subjects

One hundred twenty-eight graduate and undergraduate students (63 female, 65 male) enrolled in organizational behavior courses at the University of Utah and the University of Arizona participated as subjects. Participation was in partial fulfillment of a course requirement. Subjects ranged in age from 19 to 46 years (x = 25.9 years), and had worked full-time for a mean of 4.6 years. Subjects were provided detailed feedback about their performance and the purpose of the study in subsequent class sessions. Design

A fully crossed 2 x 2 factorial design (frame x cost salience) was employed. Framing was manipulated by varying the task instructions. In the positively framed condition, subjects were instructed to list the names of all applicants (from an applicant pool of 20 candidates) that they would “accept” for an interview. In the negatively framed condition, subjects were instructed to list the names of all applicants they would “reject” for an interview. The cost salience condition was manipulated by either explicitly specifying the selection-related costs or by leaving the selection-related costs implicit. In the high-cost-salience condition, “accept” framed subjects were told that, “Based upon the cost figures provided by management, it requires considerable managerial and support staff time to advertise, re-

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view, interview, and select job applicants. The cost per applicant increases as more applicants are accepted. For each candidate interviewed, the company would incur a cost of $300. While it is important for you to select high-quality candidates, it is also important that costs be contained.” For “reject” framed subjects, this section of the instructions was worded in terms of cost savings per applicant rejected. Subjects in the high-cost-salience condition additionally were required to compute and record either the cost (of accepted applicants) or the cost savings (of rejected applicants) for each applicant accepted or rejected. In the lowcost-salience condition, the potential costs or savings associated with applicant interviewing were not mentioned. Experimental

Materials

The experimental materials were adapted from personnel files provided by a large computer retailer. The organization was described to subjects as needing computer technician’s assistants because of significant increases in sales and service contracts. (For a more detailed explanation of the experimental materials see Huber, 1985). The organization’s actual job description for a technician’s assistant and the advertising copy used to solicit applicants also were included among the experimental materials. The newspaper advertisement indicated that “fast learners were needed to assemble, test, and install microcomputer systems.” The positions were portrayed as entry level and required no specific technical background. However, “an interest in computers and an ability to deal with people in an organized and efficient manner” was described as necessary. Resumes and letters of applications used as experimental stimuli were selected from among 200 applications actually received by the organization in response to an advertisement in a local newspaper. Applicants who were obviously overqualified (Ph.D.s), obviously underqualified (no work history), or poorly marketed (no cover letters, handwritten letters of application, extensive misspelling, etc.) were excluded. From the remaining applicant files, 20 applications were randomly selected and modified for use as experimental materials. Procedures

Upon arrival in the classroom, subjects were randomly assigned to conditions. Instructions were provided to all participants indicating that this was a human resource decision-making exercise. Experimental materials were distributed to all subjects, which described a hypothetical company “Compusystems” as one of the largest retailers of computers and auxiliary equipment in the Rocky Mountain region. Subjects were asked to assume the role of Compusystem’s human resource manager

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and read each applicant’s file (application letter and resume). After reading each of the 20 files, subjects were instructed to list the names of the applicants they would accept (or reject) for job interviews. Costs associated with the selection of an interviewee were also specified in the appropriate conditions. When each subject had completed listing the candidates she/he would accept (or reject) for an interview, the experimenter recorded the time required to complete this portion of the task. Next, subjects were asked to indicate for each applicant the “probability (lo- 100%) that a particular individual would have succeeded in the technician’s job if hired.” Finally, subjects were asked to complete a brief biographical inventory including age, education, and work experience items. Three dependent variables were examined: 1. Number of candidates accepted for interview (up to 20); 2. Decision-processing time (in minutes); 3. Acceptance thresholds for applicant selection. (This was operationalized as the lowest “probability of success” estimate among applicants selected for interviewing by each subject.) RESULTS

The means and standard deviations for the three dependent variables -number of applicants accepted for interviews, decision time, and decision criteria for applicant selection-are reported by experimental condition in Table 1. Hypothesis la predicted that negatively framed (“rejection” strategy) decision makers would accept more candidates for job interviews than positively framed (“acceptance” strategy) decision makers. Hypothesis la was supported. As shown by the analysis of variance reported in Table 2, there was a significant main effect for framing (Fo,J22) = 16.4, p < .OOOl). Negatively framed decision makers accepted significantly more TABLE

1

SUMMARY OF DEPENDENTVARIABLES: MEANS AND (STANDARD DEVIATIONS) Frame Reject Low-cost salience Applicants accepted Acceptance threshold Decision time High-cost salience Applicants accepted Acceptance threshold Decision time

Accept

10.31 (3.18)

10.13 (3.18)

56.8%

24.91 (9.47)

65.8% 23.56

(17.0) (6.07)

9.44 67.8% 29.55

5.71 74.9% 31.14

(15.0)

(19.0)

(2.95) (16.4) (9.22)

(2.17) (10.54)

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TABLE 2 ANALYSESOFVARIANCEFORAPPLICANTSACCEPTED,DECISIONTIME,ANDACCEPTANCE THRESHOLD:FRAMEANDCOSTSALIENCEASINDEPENDENTVARIABLES Dependent

variable

Source

df

Frame Cost Frame x cost

I I 1

Frame Cost Frame x cost

1 I 1

2049.2 3089.0 30.1

6.99** 10.55** NS

Frame cost Frame x cost

I I I

0.47 1195.48 69.57

NS 14.80*** NS

MS

F

Applicants accepted 128.52 215.42 98.59

16.42*** 21.52*** 12.59***

Acceptance threshold

Decision time

** p < .Ol. *** p< ,001.

applicants for interviews (x = 9.87) than positively framed decision makers (x = 7.85). Hypothesis lb also was supported. The mean success probability acceptance threshold was significantly higher when decision makers were positively framed (70.4% versus 62.2%) than when they were negatively framed (F(, 1,8I = 6.99, p < .Ol). Hypotheiis lc predicted that positively framed decision makers will take less time to make their choices than negatively framed decision makers. Hypothesis lc was not supported. As reported in Table 2, framing did not significantly influence the time to complete the task (F < 1.00, p = NS). Consistent with Hypothesis 2a, the number of applicants selected to interview was significantly influenced by the salience of selection-related = 27.52, p = .OOl). When selection-related costs were costs V&122) made salient, subjects selected significantly fewer applicants for interviews (7.57) than when costs were only implicit (10.22). Hypothesis 2b predicted that making salient selection-related costs would raise the decision criterion (acceptance threshold of success probability) for interviews. Hypothesis 2b was supported. Decision makers for whom costs were salient used a significantly higher acceptance threshold (71.4% versus 61.2%) than those decision makers for whom selection-related costs were not salient (F(,,,,,, = 7.78, p < .02). Hypothesis 2c predicted that the salience of selection-related costs will influence decision-processing time. Hypothesis 2c also was supported.

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As shown in Table 2, there was a significant main effect for cost salience on decision time (Fo122) = 14.8, p < .OOl). Thus, when selection-related costs were made salient, fewer applicants were selected and it took longer (30.3 versus 24.2 min) for those selection decisions to be made. Two additional post hoc analyses were run on the applicants-accepted and acceptance-threshold dependent measures. For the applicants-accepted measure, a post hoc contrast describing the data as two levels (high-cost salience/accept significantly different from all other conditions; no other significant differences) was significant (F,,124) = 55.38, p < .OOl). This contrast captured 97.9% of the between-cells sums of squares. For the acceptance-threshold measure, a post hoc contrast describing the data as three levels (low-cost salience/reject less than all other conditions; high-cost salience/accept greater than all other conditions; no other significant differences) was significant (F(,,,,s, = 17.04, p < .OOl). This contrast captured 96.7% of the between-cells sums of squares. DISCUSSION

The purpose of this study was to examine the impact of decisional biases on applicant selection decisions. Specifically, this study investigated the impact of framing and cost salience on both the process (decision time and acceptance threshold) and the outcome (number of applicants accepted) of decision makers in the context of personnel selection. The results of this study support the propositions that (1) the way in which a choice among job applicants is framed-either positively or negatively-biases the outcome of selection-related decisions, and (2) the salience of decision-related costs alters the processes and outcomes of selection decisions. Specifically, if decision makers used a positively framed selection strategy (“accept those applicants who should be interviewed”) rather than a negatively framed strategy (“reject those applicants who should nor be interviewed”), significantly less applicants will be interviewed. However, it appears that this is only true when selection-related costs are salient. Apparently, decision makers are more willing to take a chance on (i.e., interview) less promising applicants unless the decision maker is positively (risk averse) framed and the costs of this behavior are made salient. Another way of saying this is that selection decision makers may be more willing to drop marginal applicants from consideration prior to interviewing if positively framed when selection-related costs are salient. Judging from this study, the savings from such early cuts in the applicant pool would be substantial. The pools of applicants selected for interviews were fully 40% smaller when decision makers were positively framed and selection-related costs were salient.

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Interestingly, cost salience influenced not only the number of applicants selected (the outcome of selection decisions) but also the process (decision time) of making those selections. Thus, there appears to be a trade-off. While salient costs can result in fewer applicant interviews (if decision makers are positively framed), it will take decision makers longer to make the selections. If time is precious, then cost salience may lead to undesirable delays. Future research might explore further the trade-off between these two sources of costs. The findings of this study also indicate that cost salience and framing each independently influence decision makers’ acceptance thresholds for applicant selection. When costs are made salient or acceptance strategies are used, decision makers become more conservative (i.e., risk averse) and select applicants for whom the perceived probability of success in the position (“what is the probability that this person will succeed if hired”) is greater. In contrast to the findings for numbers of applicants accepted for interviews, costs apparently do not need to be made salient for framing to influence applicant acceptance thresholds. Why acceptance threshold framing effects did not lead to commensurate framing effects in numbers of applicants accepted when costs were not salient remains as a puzzle for future researchers. At the very least this finding implies that applicant success probability is not the only criterion used for applicant selection decisions. Previous research suggests that it takes longer to process negatively framed than positively framed information (Wason, 1959). In this study, however, frame (“reject” or “accept” selection strategy) had no effect on the amount of time it took decision makers to reach a selection decision. This suggests that negatively framing a decision maker is qualitatively different from having that decision maker “think” in negative terms. While the results of this study suggest that framing may lead human resource decision makers to adopt different selection strategies, this conclusion should be viewed with caution. The decision-making processes of college students may not completely generalize to more expert selectiondecision populations. However, it should be noted that selection decisions often are made by individuals with limited amounts of selection-decision expertise. Faculty members, entrepreneurs, small-business owners, membership committees of private organizations, and other selection-decision novices often are saddled with the task of screening applicants for organizational inclusion. In fact, in the organization from which the experimental materials were taken, no training of store managers for selection-related activities was provided by the franchiser. Further, the experimental materials used in this study were adapted from the organization’s actual selection process. Consequently, the materials used

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in the study have considerable ecological validity for large computer retailing organizations. Thus, while concerns about external validity may be warranted in this case, threats to the generalizability of these findings probably are less compelling than in other settings. While there has been considerable application of behavioral decision theory in such areas as bargaining and negotiation (Neale & Bazerman, 1985), medical decision making (Christensen-Szylanski, Beck, Christensen-Szylanski, & Koepsell, 1983), judicial decision making (Loftus, 1979), and clinical psychology (Dawes, 1976), the area of human resource management has, for the most part, been ignored. The findings of this study signal the need for further exploration of the role of decisional biases and judgmental processes in a variety of human resource management settings. With the increasing attention to and the potential benefit of improved decision making in the human resource function of organizations, it seems that extending the findings of behavioral decision theory to this context is imperative. Finally, within a decision-making perspective, a wide array of future research concerned with human resource management becomes available. Since the very nature of human resource management is a series of choices (including recruiting, hiring, evaluating, promoting, transfering, demoting, terminating), the foibles and shortcomings of human decision makers are of critical importance. Extending our understanding of, and developing aids to reduce, the influence of decisional biases on selection decisions is one way in which decision makers can improve their skill in selection-related judgments. REFERENCES Brogden, H. (1949). When testing pays off. Personnel Psychology, 2, 171-183. Cascio, W. (1980). Responding to the demand for accountability: A critical analysis of three utility models. Organizational Behavior and Human Performance, 25, 32-45. Christensen-Szylanski, J. J., Beck, D., Christensen-Szylanski, C., & Koepsell, T. (1983). Effects of expertise and experience on risk judgments. Journal of Applied Psychology, 63, 278-284. Connolly, T., & Serre, P. (1984). Information search in judgment tasks: The effects of unequal cue validity and cost. Organizational Behavior and Human Performance, 34, 387-401. Cronbach, L. J., & Gleser, G. (1965). Psychological tests andpersonnel decisions. Urbana, IL: Univ. of Illinois Press. Cronbach, L. J., Yalow, E., & Schaeffer, G. (1980). A mathematical structure for analyzing fairness in selection. Personnel Psychology, 33, 693-704. Dawes, R. (1976). Shallow psychology. In J. Carroll & J. W. Payne (Eds.), Cognition and social behavior. Hillsdale, NJ: Erlbaum. Gordon, J. R. (1986). Human resource management. Boston: Allyn & Bacon. Huber, V. L. (1985). Compusystems job search. In R. Schuler & S. Youngblood (Eds.), Case problems in personnel and human management (pp. 42-51). New York: West.

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Kahneman, D., Slavic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge Univ. Press. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under risk. Econometrica,

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Landy, F. J., Farr, J. L., & Jacobs, R. R. (1982). Utility concepts in performance measurement. Organizational Behavior and Human Performance, 30, 15-40. Ledvinka, J. (1979). The statistical definition of fairness in the federal selection guidelines and its implications for minority employment. Personnel Psychology, 32, 551-562. Loftus, E. (1979). Eyewitness testimony. Cambridge, MA: Harvard Univ. Press. McCormick, E. J. (1976). Job and task analysis. In M. Dunnette (Ed.), Handbook ofindustrial and organizational psychology (pp. 651-696). Chicago: Rand McNally. Neale, M. A. (1984). The effects of negotiation and arbitration cost salience on bargainer behavior: The role of the arbitrator and constituency on negotiator judgment. Organizational Behavior and Human Performance, 34, 97- 111. Neale, M. A., & Bazerman, M. H. (1985). Perspectives for understanding negotiation: Viewing negotiation as a judgmental process. Journal of Conflict Resolution, 29, 33-55. Neale, M. A., Huber, V. L., & Northcraft, G. B. (1987). The framing of negotiations: Contextual versus task frames. Organizational Behavior and Human Decision Processes. Northcraft, G. B., & Neale, M. A. (1986). Opportunity costs and the framing of resource allocation decisions. Organizational Behavior und Human Decision Processes, 37. 348-356. Oskamp, S. (1965). Overconfidence in case-study judgments. Journal of Consulting Psychology, 29, 261-265. Schmidt, F. L., & Hunter, J. E. (1981). Research finding in personnel selection: Myths meet Policies and Procedures,for realities in the 1980’s. In Public Personnel Administration: Personnel, New York: Prentice-Hall. Schmidt, F. L., & Hunter, J. E. (1983). Employment testing: Old theories and new research findings. In G. F. Dreher & P. R. Sackett (Eds.), Perspectus on employee staffing und selection. Homewood, IL: Irwin. Schmidt, F. L., Hunter, J. E.. & Pearlman, K. (1982). Assessing the economic impact of personnel programs on productivity. Personnel Psychology, Summer, 238-248. Schneider, B. (1976). Stuffing organizations. Pacific Palisades, CA: Goodyear Publishing. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207-232. Van Rijn, P. (1976). Job analysis for selection: An overlieu,. U.S. Office of Personnel Management, Examination Services Branch. Wason, P. C. (1959). The processing of positive and negative information. Quarterly Journal of Experimental Psychology. 11, 92-107. RECEIVED: May 5, 1986