Personality Testing and Industrial Organizational Psychology: A Productive Exchange and Some Future Directions

Industrial and Organizational Psychology, 1 (2008), 323 332. Copyright ª 2008 Society for Industrial and Organizational Psychology. 1754-9426/08 RESPONSE Personality Testing and Industrial Organizational Psychology: A Productive Exchange and Some Future Directions FREDERICK L. OSWALD Michigan State University LEAETTA M. HOUGH The Dunnette Group, Ltd. Abstract The goal of our focal article was to provide a current perspective on personality testing and its use in organizational research and to elicit constructive discussion and suggestions for future research and practice. The present article caps off the discussion by integrating the main ideas presented in the commentaries within our original framework of questions and topics, with the immodest hope of advancing our understanding of personality and its measurement in the context of industrial organizational psychology. In short, we recommend continuing to take advantage of the organizing framework of the Big Five while also pursuing more bottom-up approaches that examine facet-level relationships with multidimensional performance outcomes, in addition to developing process models that include more proximal motivational and situational variables. Work along these lines is valuable to both organizational science and practice. Correspondence concerning this article should be addressed to Fred Oswald, E-mail: foswald@rice.edu Address: Rice University, Department of Psychology, 6100 Main Street, MS 205, Houston, TX 77007 Frederick L. Oswald, Department of Psychology, Michigan State University; Leaetta M. Hough, The Dunnette Group, Ltd. In our focal article (Hough & Oswald, 2008), our goal was to provide a current perspective on personality testing and its use in organizational research and to elicit constructive discussion and suggestions for future research and practice. We are thrilled with the wide range of insightful commentary that our article inspired, and more generally that this new Society for Industrial and Organizational Psychology s journal is creating a useful and exciting intellectual exchange. Below, we have integrated the ideas presented in the commentaries into our original framework of questions and topics, comparing and contrasting the differing and sometimes opposing points of view. Multidimensional Models of Job Performance, Personality Measurement, and Taxonomic Issues We noted in our focal article that (a) multidimensional models of job performance had become more refined over the past 2 decades, in particular incorporating more personality-relevant performance constructs; (b) such refinement should enable us to focus more precisely on those personality constructs that enhance our understanding of the relationships between personality variables and performance; (c) as a taxonomic structure, each factor of the five-factor model of personality is usually too heterogeneous, a facet-level approach to validity is a bottom-up approach that can tell us empirically whether it is more useful than the 323

324 F.L. Oswald and L.M. Hough five-factor model in building our science and practice; (d) additional theory-based and process-based variables need to be incorporated into our models relating personality to performance; and (e) we should build detailed cumulative databases that contain information about personality and criterion relationships, systematically organized by measurement method, situation, and other known moderator variables. Stewart (2008) suggests that to focus on narrower personality constructs, as we have encouraged, is to sacrifice or at least turn a blind eye to the validity that has been found for broader personality constructs predicting broad performance behaviors. We agree that from a practical perspective, broad personality traits can be predictive of similarly broad work behaviors (Hough & Ones, 2001). In fact, the more complex the performance criterion is, the more multifaceted the personality predictor may have to be (Hogan & Roberts, 1996; Hough & Ones, 2001). The prediction afforded by broad personality constructs, however, does not preclude the need for additional research on relationships between personality facets and performance dimensions. Do all facets of Conscientiousness contribute to the prediction of absenteeism or are only some of them responsible? Do different facets of Conscientiousness predict attention to detail for detail-oriented jobs? and Do facets of Conscientiousness help us better understand the mediating and moderating variables that qualify or explain Conscientiousness performance relationships? Research involving facet-level personality measures and multidimensional performance outcomes helps address questions like these, and it also helps to make very general statements about the Big Five factors predicting job performance more useful and precise. In other words, both practice and theory can improve when we understand better exactly how and when broad traits are predictive. Personality-based job analysis has led to developing performance criteria that are predicted better by facets than by broader constructs (Jenkins & Griffith, 2004). But neither Stewart nor you, gentle reader, should mistake us: We are not suggesting, for instance, that traits be conceptualized and measured on the independent variable side so narrowly that personality items essentially repeat the same question just to raise the alpha level or that job performance on the dependent variable side be broken down into constituent parts that are microscopic or mechanistic. Furthermore, we agree with Stewart that personality traits are especially predictive in the sort of open social and teamwork environments that he describes; however, we argue that an empirical understanding that facets are driving prediction by the Big Five can be illuminating. We are not suggesting an infinite regress of going narrow. Conceptually and practically we, like other researchers (e.g., Paunonen, Rothstein, & Jackson, 1999; Schneider, Hough, & Dunnette, 1999; van Iddekinge, Taylor, & Eidson, 2005), think that a bottom-up approach to understanding personality at the facet-level complements the Big Five top-down approach and may help us understand how and when personality is predictive of performance outcomes, such as for the traits of Conscientiousness (Roberts, Bogg, Walton, Chernyshenko, & Stark, 2004; Roberts, Chernyshenko, Stark, & Goldberg, 2005) and Openness to Experience (Chernyshenko, Stark, Woo, & Conz, 2008). As Barrett (2008) notes, facets can have low correlations between one another within the same Big Five construct and hence are empirically distinguishable, leaving the door open to finding useful patterns of differential validity (see also Hough, 1992). We clearly agree with Barrett that Conscientiousness is too broad and heterogeneous of a factor to consider it as a unitary and consistently valid predictor across all occupations and criteria. The broad construct of Conscientiousness is not highly predictive of creative outcomes, for example (Feist, 1998; Hough, 1992; Hough & Dilchert, 2007; Hough & Furnham, 2003), and the Conscientiousness facets of dependability and achievement have demonstrated differential prediction for performance outcomes at both the individual and team levels (LePine, 2003; LePine, Colquitt, & Erez, 2000). Other differential relationships

Personality testing and industrial organizational psychology: Response 325 between personality facets and performance were discussed in our focal article. Although arguing for more bottom-up research on personality, we also acknowledge that for practical and/or theoretical reasons, it is likely that some personality constructs are more profitably explored at a facet level than are others. For instance, a meta-analysis has indicated that some facets of Conscientiousness (e.g., achievement, dependability and order) are found to provide incremental validity for some combinations of job types and performance criterion but not for others (Dudley, Orvis, Liebecki, & Cortina, 2006). Similarly, the facets of Extraversion (e.g., dominance, sociability, and energy level) have shown differential relationships with criteria (Hough, 1992; Hough, Ones, & Viswesvaran, 1998). Facets of Agreeableness, on the other hand, do not appear to exhibit such differential relationships. Hence, broad Agreeableness may be useful for predicting the social aspects of work, but facets of Extraversion may be more useful to this end. It is also worth considering that methods other than self-report for collecting personality data may lead to different patterns of empirical distinctiveness and incremental validity at the facet level. Barrett is skeptical of meta-analytic results on personality performance relationships, for one because by contrast, practitioners want to obtain the best validity coefficients for particular measures and in a particular occupational setting. It is true that different personality scales do comprise different sets of items, and therefore some organizations may be especially interested in the specific measures they use. But from a theoretical standpoint, these items and scales are indicators of constructs, and meta-analyses that have organized personality scales at the construct and facet levels of the Big Five have provided theoretically useful and important information concerning criterion-related validity. A measure-specific approach can also be useful, but if followed exclusively, it could severely hinder our field as a science. We do not want to return to the good old daze (Hough, 1997) of the past with as many personality constructs as we have scales. Current progress has instead been made through several major metaanalyses of different personality scales, applied in organizational contexts, that have provided our field with knowledge that generalizes. There is a fair middle ground between broad meta-analyses and scalespecific analyses that is consistent with Barrett s concerns: developing databases and meta-analyses of more refined personality and performance constructs, such as facets of the Big Five and types of counterproductive work behavior (see Hough & Ones, 2001, for a nomological-web clustering approach). Johnson (2008) takes this more refined approach when he reviews and integrates theoretical models that involve substantive moderators and mediators that explain variance in and across personality performance relationships. The Johnson and Hezlett (2008) model that he describes is a demonstration of the complexity of factors and variables that determine performance. Clearly, the world of work is not bivariate, and their model confirms our original comment that the complexity of the nomological nets of personality constructs is enormous. They are, thankfully, much more specific than we were: Their model contains variables related to motivation that, are relatively distal (e.g., work attitudes) and proximal (e.g., self-regulation) with respect to job performance outcomes. Models like these are important because they suggest that motivational variables more proximal to performance are meaningful (see also Humphreys & Revelle, 1984; Kanfer, 1990) and also because they make explicit the fact that not all relevant variables in a performance model may be available in the practice of personnel selection. For instance, Johnson s model indicates that on-the-job stress will show negative correlations with job performance, but in a selection setting, a more distal measure of general stress tolerance may have to suffice for understanding that particular relationship in a job applicant sample. We concur with Stewart that we also need good models of the determinants of team performance, and models of individual

326 F.L. Oswald and L.M. Hough performance are not enough in many cases. We reiterate our point that if we understand the nomological net of narrower (e.g., Big Five facet-level) variables, we are more likely to combine these facets more strategically and efficiently into broader variables that are useful for both individual- and team-level performance models, thereby advancing both our science and practice. Composition models suggest that such variables may differ for individual- and teamlevel performance models (Chan, 1998), but at each level, they are composed of well-understood facet-level personality variables. We are suggesting that measures of facet-level constructs remain intact; they are simply configured differently at the broader level, depending upon factors such as the level of analysis and the dependent variables figuring into the performance model of interest. Regarding measurement in personality, multiple items (or other measures) that sample content representatively from the relevant construct domain are required for item covariances to build up geometrically and overwhelm the idiosyncrasies of itemspecific variance. The heterogeneity of content sampled by items tends toward lowering alpha reliability, but sampling a large number of such items tends to raise it (Little, Lindenberger, & Nesselroade, 1999). Understanding the nature of content heterogeneity is critical when sampling items to form a measure of a broad construct, such as a Big Five personality construct, and to us that often means understanding the reliability and validity of its facets. This same argument applies to defining a facet carefully such that content sampling is appropriate at the facet level as well (see Comrey, 1988; Hogan, 1983). We believe a consensus is emerging: As we refine our conceptualization and measurement of the dependent variables of critical importance to organizations, the science and practice of industrial organizational psychology will benefit from conceptualizing and measuring most personality variables at a commensurate level not too far off from the level of specificity of the facets of the fivefactor model of personality (e.g., Paunonen, 1998; Paunonen & Ashton, 2001; Roberts et al., 2005). Personality constructs measured at this level will become increasingly important in models, measures, and metaanalyses of individual- and team-level job performance. Importance of Situations In our focal article, we asserted that (a) situations often moderate or mediate relationships between personality constructs and work performance at both the individual and the team levels, (b) more attention to the situation is needed in the measurement of personality constructs, (c) more attention to the situation is needed in our models of performance, and (d) our science and practice would be aided by the development of a taxonomy of situations. Christiansen and Tett (2008) cogently detail the need to examine the effects of situational characteristics on the expression of personality-relevant behavior because research to date has found moderate and heterogeneous levels of validity in personality performance relationships. Situational characteristics, if they are investigated, often lack the detail that the authors would recommend. From a psychological point of view, it may be profitable to measure situations more directly in terms of personality, in terms of how much choice behavior is allowable, how much responsibility is provided, and so on. Personalityrelevant performance data are often too coarse (e.g., a cursory supervisory performance appraisal); better data may come from multiple-perspective feedback instruments that provide different types of data (e.g., data on teamwork/helping behavior from peer ratings; data on customer interactions from the customer and employee ratings themselves). Also the O*NET may provide a good start, as the authors note. Hierarchical linear models (HLM) incorporate and test relationships at the individual level, situational level, and crosslevel interactions: Personality-relevant situational characteristics at the job, social,

Personality testing and industrial organizational psychology: Response 327 and organizational levels can be modeled to predict differences in personality performance relationships across jobs. Strong and weak situations (of various types) exert range-restricting and range-enhancing effects (see Ackerman, Kanfer, & Goff, 1995; Beaty, Cleveland, & Murphy, 2001; Klehe & Anderson, 2007; Ployhart, Lim, & Chan, 2001), and HLM can inform how those effects lead to differences in mean performance and criterion-validity across jobs or departments and over time (see Voelkle, Wittmann, & Ackerman, 2006, for a longitudinal example). To argue for the importance of the situation, Christiansen and Tett focus on the SDq estimates from meta-analysis of personality performance relationships as evidence of heterogeneity that mitigates overall metaanalytic findings. We did not focus on these estimates by intent, not by accident: Simulation research has found SDq estimates to be highly inaccurate in terms of generating both Type I and Type II errors, leading to false conclusions about validity generalization or a lack thereof (see Cornwell & Ladd, 1993; Kemery, Mossholder, & Dunlap, 1989; Oswald & Johnson, 1998). This research is relevant for all meta-analyses not just those in the personality domain. A brief story provides a second reason not to focus on SDq: when the first author was teething on articles in graduate school, he read the now-famous Barrick and Mount (1991) meta-analysis of the Big Five. Being curious, he requested and received from Barrick a list of the personality measures assigned to Big Five constructs, and what was discovered supports the point of Barrett s: The personality measures were quite diverse in nature, and several of the measures were narrower than the broad Big Five constructs to which they were assigned (for instance, validity involving a measure of dependability may have been assigned to the umbrella construct of Conscientiousness). Thus, although meta-analytically averaged validities organized into Big Five categories are eminently useful for summarizing research, the aforementioned construct deficiencies can lead to systematic increases, decreases, or heterogeneity in validity that cannot be appropriately accounted for statistically by SDq or any correction factor; they must be examined substantively by conducting research that examines situations and the narrower facet level at which the personality measures were derived. Barrett also cites differences in job types, concurrent versus predictive validities, and faking effects across studies as other sources of heterogeneity underlying meta-analysis; the nature of these differences and other important ones (e.g., leadership style; type of performance criterion) also must be researched directly and not be guided by the statistical estimate of SDq associated with an overall meta-analytic personality performance correlation. In general, we strongly encourage studies and meta-analyses of important moderators of personality performance relationships no matter what the value of SDq is estimated to be. Heggestad and Gordon (2008) cite severalrecentarticlesthatreportincreased validity for personality measures that are contextualized to focus on the workplace. White, Young, Hunter, and Rumsey (2008), however, come to a different conclusion with regard to research adopting a greater focus on context. They point out that in their large sample military studies, items that are less content relevant (less contextualized) have greater criterion-related validity in longitudinal studies and less validity in concurrent-related validity studies. Their findings serve as an important caveat in the use of personality measures in personnel selection, and they suggest that items that are less obvious about what characteristic is being measured retain their validity in real-life applicant settings. A similar argument has been made for conditional reasoning measures of personality (James, 1998). The Stewart discussion of team tasks involving team-member interdependency also addresses the situation as a moderator of the relationships between personality variables and criteria. We would add that a team task requiring a creative solution versus a routine solution is another example of a situational moderator where personality variables will show different

328 F.L. Oswald and L.M. Hough patterns of prediction (Hough, 1992; Hough & Dilchert, 2007). Personality Variables and Incremental Validity Barrett appears to question whether personality variables will increase the accuracy of prediction of work performance when combined with measures of other characteristics such as cognitive ability. Empirical research from Project A (see White et al.) involving thousands of soldiers found that personality variables often provide a modest but important increment to validity when combined with measures of cognitive ability, with no reason to suggest that such findings would not exist in the civilian population as well. In fact, dozens of other empirical studies have found similar results in nonmilitary settings. Second, what Barrett refers to as simulation studies of the increment in validity resulting from the combination of personality and cognitive variables are, in fact, mathematical calculations based on metaanalytic estimates, which in turn are not simulated but instead are based on cumulative research evidence. We do not pretend that these are perfect or universal estimates, as we have previously noted, but they are informative. Similarly, all the other commentators to our focal article are clearly persuaded by the accumulated evidence that personality variables offer the possibility of increasing the overall accuracy of prediction of work performance in general and when combined with measures of cognitive ability. To be fair with regard to this issue of incremental validity, we note that empirical comparisons of broad versus narrow personality constructs in regression models need to account for the fact that models with facets will contain a greater number of variables and thus have greater chance of capitalizing on chance. Therefore, model results comparing broad versus narrow personality variables should be cross validated or made comparable in other appropriate ways (Paunonen & Ashton, 2001; Tett, Steele, & Beauregard, 2003). Measurement Methods and Faking We are heartened that researchers are currently developing and investigating alternative strategies to typical self-report measures using a Likert-type or yes/no format. The work of White et al. has provided significant insights and advances in our knowledge about the measurement of personality constructs and ways to address problems with self-report measures. Their commentary is a must-read for academics and practitioners alike. White et al. reflect on the contributions of Project A and related military research that has followed. The Army s Assessment of Individual Motivation (AIM) led the resurgence of examining forced-choice personality measures in the attempt to reduce faking in high-stakes personality testing. In a similar vein, the U.S. Navy has recently developed a set of unidimensional forced-choice measures called the Navy Computer Adaptive Personality System (NCAPS; see Houston, Borman, Farmer, & Bearden, 2006). Although forced-choice measures induce negative intercorrelations induced by the dependence between item responses (Hicks, 1970), the computerized nature of forced-choice measures provides several potential advantages. First, randomized item pairings help increase test security. Second, the adaptive nature of the measure presents items to the test-taker based on previous responses, thereby allowing fewer items to be administered while maintaining high reliability. Third, statistical modeling and scoring tools that account for the aforementioned dependencies in these types of data are available, though large sample sizes (at least 450) are required for stable estimation (Stark & Drasgow, 2002; also see Cheung, 2004; Maydeu-Olivares & Böckenholt, 2005). As White et al. note, forced-choice measures appear to show promise by retaining higher levels of validity in high-stakes situations that tend to reduce criterionrelated validity for Likert-scale personality measures (see also Jackson, Wrobleski, & Ashton, 2000).

Personality testing and industrial organizational psychology: Response 329 Faking on Likert-scale measures continues to receive research attention. White et al. found that items conceptually linked to job performance, but low in job content (low context), result in predictive validity coefficients in high-stakes testing situations that are similar to concurrent validity coefficients, perhaps because the low job content is less prone to faking. Kuncel and Borneman (2007) used idiosyncratic item responses to detect deliberately faked personality inventories. Their method successfully classified about one-fifth to one-third of intentionally distorted responses to personality items with only a 1% false-positive rate in a sample of approximately 50% honest respondents. Griffith and Peterson (2008) provide a thoughtful and detailed analysis comparing social desirability measures with the faking behavior on personality tests that they are intended to predict. As they note, more proactive approaches to personality testing would appear to be more effective in reducing faking than the reactive approach of attempting to detect faking after it occurred. Regarding the latter, we are not alone in believing that social desirability scales to detect faking are problematic, for reasons described in our focal article. The Paulhus Balanced Inventory of Desirable Responding (Paulhus, 1998) is often used as a measure self-deception and impression management dimensions of social desirability. After reviewing the research since his influential article on social desirability (Paulhus, 1984), Paulhus himself concluded (in Paulhus & John, 1998) that self-deception and impression management are more usefully considered within a broader framework of self-perception than instantiated as measures to control for faking personality measures. Specifically, self-deception is related to an egoistic tendency to exaggerate one s ability or status and is correlated with Extraversion, Openness, and Neuroticism; impression management is related to a moralistic tendency to exaggerate one s goodness as a member of society and is correlated with Agreeableness and Conscientiousness. These so-called biasing mechanisms may be adaptive, in general and specifically, in predicting work performance. In taking a more proactive approach to controlling for faking, three critical areas for research come to mind: having test-takers understand the personality test within the larger context of person job fit, other tests in the test battery, and an organization s personnel selection process; providing warnings not to fake (or positive warnings to respond honestly); and examining test formats such as the forced-choice formats previously mentioned. Converse et al. (2008), have examined the latter two factors, noting that despite the potential advantages of these testing interventions, it may come at the cost of an increase in negative test-taker reactions. Certainly, more research in all three of these areas would be viewed as more productive than continuing individual-differences work on social desirability measurement or experimental work in instructed faking. We add that future research would benefit from a critical review and framework to classify the key characteristics that distinguish high-stakes testing situations that are likely to induce or reduce test faking. Legal Issues Jones and Arnold (2008) remind us about the possibility that, in practice, personality testing might be significantly restricted and even prohibited under state and federal law. They provide important cautions and information about how to avoid organizational exposure to risk when incorporating personality tests into selection systems, including the need to remain vigilant about marketing claims. Barrett also addresses legal issues in his commentary, more specifically arguments made by plaintiffs and defendants experts. According to Barrett, plaintiff s experts often argue, without providing empirical evidence, that a self-report personality test in combination with a cognitive ability test will reduce adverse impact against protected classes. A significant amount of such evidence already exists in the literature to allow for some safe bets about the likely adverse

330 F.L. Oswald and L.M. Hough impact of a scale when used operationally, and White et al. provide empirical evidence with sample sizes in the thousands. Hough, Oswald, and Ployhart (2001) summarized mean score differences between Whites and protected classes and men and women at broadly and more narrowly defined construct levels. They concluded: Research clearly indicates that the setting, the sample, the construct and the level of construct specificity can all, either individually or in combination, moderate the magnitude of differences between groups. Employers using tests in employment settings need to assess accurately the requirements of work. When the exact nature of work is specified, the appropriate predictors may or may not have adverse impact against some groups. (p. 152) Adverse impact concerns are yet another reason for the importance of accumulating information about mean scores and criterion-related validities at a narrower construct level than the factors in the five factor model of personality. Subgroup mean differences across facets have implications for the facets one might choose to combine when creating a predictor composite. Barrett commented that the U.S. Department of Justice (DOJ) has a history of advocating for the sole use of personality tests as an alternative to cognitive ability tests for the selection of safety forces. DOJ s actual history is to argue that personality in combination with cognitive ability is a preferable alternative to the use of cognitive ability tests alone, although the court never ruled on the alternatives part of the City of Garland, TX, case to which Barrett refers. Conclusions This exchange on the role of personality testing in organizational research has stimulated our own thinking and hopefully yours as well. We have taken the position that, although meta-analyses of the Big Five have yielded useful empirical results, and the Big Five remains a useful organizing framework, that facet-level personality research should become very productive these days, given that the job performance domain on the dependent variable side of the equation has become more refined and multidimensional than ever before. Moreover, process models that incorporate motivational mediators and situational moderators will also benefit greatly from empirical and theoretical work taking a facet-level approach to personality. Situational characteristics in such models deserve to be measured in their own right rather than by proxy, so that situational moderating effects are modeled appropriately. Furthermore, process models will be enhanced by incorporating the dimension of time, and continued use of longitudinal analytic methods will inform how and when personality prediction unfolds over the course of time (see Chan & Schmitt, 2000; Schmitt, Oswald, Friede, Imus, & Merritt, 2008). Our maturing development of personality theory and measurement in organizational research cannot be blind to the real-world context in which measurement occurs. The high-stakes context of personnel selection means that test faking will continue to be a topic worthy of pursuit, and we noted particularly productive and unproductive directions for future research. The legal context also must not be ignored, and the pressure to ensure the job relevance of personality measures administered as part of a selection battery of tests will continue to mount. We want to close with our appreciation for the opportunity to participate with our commentators in the scholarly interchange of this new journal format, and we hope to have done no less than to inform and inspire those involved in organizational research and practice in personality testing, at least by some small amount. References Ackerman, P. L., Kanfer, R., & Goff, M. (1995). Cognitive and noncognitive determinants and consequences of complex skill acquisition. Journal of Experimental Psychology: Applied, 1, 270 304.

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