- Email: [email protected]
Gender differences in personality scores: Implications for differential hiring rates
Personality and Individual Differences 50 (2011) 106–110
Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Gender differences in personality scores: Implications for differential hiring rates Deborah M. Powell a,⇑, Richard D. Goffin b, Ian R. Gellatly c a
Department of Psychology, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Department of Psychology, The University of Western Ontario, London, Ontario, Canada N6A 5C2 c School of Business, University of Alberta, Edmonton, Canada T6G 2R6 b
a r t i c l e
i n f o
Article history: Received 21 June 2010 Received in revised form 2 September 2010 Accepted 7 September 2010
Keywords: Personnel selection Adverse impact Five Factor Model Personality testing
a b s t r a c t We investigated the extent to which gender differences in personality test scores used in a personnel selection context are likely to cause differential hiring rates. Participants were candidates (N = 572) applying for positions at an oil refinery. Candidates completed both facet-level and broad Five Factor Model (FFM) personality measures. Focusing on the FFM broad traits of Extraversion and Conscientiousness, we found that generally there was less adverse impact when using FFM broad traits as compared to facet-level traits. When facet-level traits were reflective of agency – an underlying need to be autonomous, or communion – a need to be part of a larger social entity, they led to differential hiring rates for men or women. This problem was greatly reduced when using the FFM broad traits. When used for personnel selection, the use of FFM broad traits may help to promote gender diversity in the workplace. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction A recent report by the UK Women and Work Commission (2006) in February, discussed the problem of ‘occupational segregation’, that is, where one gender predominates in a particular segment of the workforce. This segregation can be problematic if, for example, a concentration of women in lower paying occupations contributes to an overall gender pay gap, or if excluding one gender from a particular occupation inadvertently creates labour shortages. One way to address this problem of occupational segregation, and therefore promote gender diversity, is through examining personnel selection tools to determine if the way that job candidates are selected facilitates gender diversity. Some personnel selection tools, such spatial ability tests, or tests of physical strength, show evidence of gender differences in scores. When one group scores lower, on average, on a given selection tool, that group is likely to be selected at a lower rate, thus creating barriers to organizational entry for the lower scoring group. When the selection rate for a protected group is lower than that for the relevant comparison group, adverse impact is said to have occurred (Uniform Guidelines on Employee Selection, 1978). Personality measures are often cited as selection tools that do not cause differential selection rates for protected groups (Hough, 1997) yet still show evidence of criterion-related validity (Barrick & Mount, 1991; Tett, Jackson, & Rothstein, 1991). Despite the claim that personality measures do not cause adverse impact, ⇑ Corresponding author. Tel.: +1 519 824 4120x52167; fax: +1 519 837 8629. E-mail addresses: [email protected] (D.M. Powell), goffi[email protected] (R.D. Goffin), [email protected] (I.R. Gellatly). 0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2010.09.010
demographic group differences in personality traits do exist. Several researchers have documented gender differences in personality traits, particularly at the facet level (e.g., Feingold, 1994; Hough, Oswald, & Ployhart, 2001). In contrast, when reported at the broad Five Factor Model (FFM) level, gender differences are generally reported to be negligible (e.g., Ones & Anderson, 2002). One issue that has yet to be fully addressed by research is the extent to which gender differences in personality scores are likely to cause differential hiring rates in personnel selection, and whether the use of facet-level traits versus FFM broad traits affects differential hiring rates. It is this issue that we address in the paper. 1.1. Theoretical framework: agency and communion Bakan (1966) theoretical framework of agency and communion may help in the explication of the basis of gender differences in personality measures. Wiggins (1991) described agency as ‘‘the condition of being a differentiated individual and it is evident in strivings for mastery and power which enhance and protect that differentiation” (p. 89). In contrast, communion is described as ‘‘the condition of being part of a larger social or spiritual entity, and it is manifested in strivings for intimacy, union, and solidarity with that larger entity” (p. 89). Agency and communion were proposed as underlying human needs. Social scientists have proposed that the underlying needs of agency and communion may be reflected in corresponding personality traits that facilitate the satisfaction of those needs (Wiggins, 1991; Wiggins & Trapnell, 1996). Indeed, Saucier and Goldberg (2003) noted that in lexical studies of personality, a two-factor structure is commonly found that is consistent with Bakan’s theory.
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
These two factors have also been called ‘‘getting ahead” and ‘‘getting along” by Hogan and Shelton (1998). Wiggins (1991) argued that historically, the concepts of agency and communion have been associated with men and women, respectively. Based on this framework, we would predict that women would score lower on measures of agentic traits, and such traits might therefore cause adverse impact if used in a selection context, and that men would score lower on communal traits, which would cause adverse impact for men. Wiggins and Trapnell (1996) argued that manifestations of both agency and communion can be identified within several of the FFM factors. For example, a facet-level trait such as Achievement Striving, which is part of Conscientiousness, can be categorized as an agentic facet, whereas other Conscientiousness facets (e.g., Dutifulness) can be categorized as communal. Although many facets can be categorized into agency or communion, the interpretation of FFM factors as agentic or communal is less clear. Based on the argument that both agentic and communal facet-level traits make up the FFM broad traits, we would predict that when assessed as FFM broad traits, personality measures will not lead to gender differences. Previous work has not investigated whether the use of FFM broad traits versus facet-level traits in a selection context would have implications for differential hiring rates of men and women. This study makes a unique contribution because we explore the use of facet-level agentic or communal traits versus FFM broad traits, in terms of the effect on differential selection of women and men in an applied selection scenario. Previous research (e.g., Hough et al., 2001) has looked primarily at mean differences between men and women. In this study, we investigated whether mean differences would translate into meaningful differences in hiring rates in an actual selection context. In the following sections, we describe research suggesting that gender differences may be reduced when using FFM broad traits, and we outline our study hypotheses.
107
indicate that the SFPQ is representative of other popular FFM measures such as the NEO-PI-R (Costa & McCrae, 1992), with correlations between corresponding factors ranging from .22 (NEO Neuroticism and SFPQ Independence) to .71 (NEO Extraversion and SFPQ Extraversion). Thus, results from the SFPQ can be expected to generalize to other FFM measures. Based on Hough et al. (2001) findings regarding gender differences in the facets of Extraversion and Conscientiousness specifically, we focused only on these two factors of the FFM. Moreover, although our study was conducted in one specific workplace, Barrick, Mount, and Judge (2001) reported that Conscientiousness is a valid predictor of performance across a variety of criterion types and occupational groups, and also reported that Extraversion was a strong predictor of success in training. Because our study was conducted in a high-tech facility, constant training was required to keep up with new technology. In fact, compensation was directly linked to skills updating, and thus Extraversion would be important for success in this organization. As well, Barrick and Mount (1991) meta-analysis found that, among FFM factors, Conscientiousness and Extraversion were, overall, the two strongest predictors of job-relevant criteria. With respect to the FFM trait of Conscientiousness, the SFPQ, in consonance with empirical findings (Jackson, Paunonen, Fraboni, & Goffin, 1996), provides two main constituent scales; namely, Industriousness and Methodicalness. Industriousness, defined by terms such as ‘‘striving, determined, and unrelenting in work habits”, is a trait that seems consistent with agency. Methodicalness, defined by terms such as ‘‘organized, neat, and thinks before acting” likely does not fall clearly under either agency or communion. Hough et al. (2001) reported that men tend to score higher on Achievement (similar to Industriousness). Based on empirical findings of Hough et al. (2001), which are consistent with our theoretical argument that Industriousness is an agentic trait whereas Methodicalness is not, we propose the following hypothesis:
1.2. Evidence of gender differences When reported at the FFM level, male/female differences in personality traits have generally been found to be negligible. For example, Ones and Anderson (2002) reported no large gender differences across three different FFM personality inventories in a large sample of British University students. Hough et al. (2001), however, reported that a difference did appear in one of the facet-level traits of Conscientiousness; women tended to score higher than men on Dependability but about the same as men on Achievement. Hough et al. (2001) also reported differences in the facets that make up Extraversion; women scored lower on average than men on Dominance but higher than men on Affiliation – a finding that is consistent with the proposition that women tend to score lower on agentic traits. It seems that examining traits at the facet level could potentially reveal gender differences that cancel out at the FFM level, at least for the factors of Extraversion and Conscientiousness. In a similar vein, a meta-analysis found that a more complete picture of racial subgroup differences was evident from analyses conducted at the facet level as opposed to the FFM level (Foldes, Duehr, & Ones, 2008). Facet-level differences in the mean scores of different groups may be averaged out at the FFM level, meaning that although group differences may exist, they are less apparent when traits are measured as FFM broad traits.
Hypothesis 1. There will be more evidence of differential selection by gender when scores on the agentic trait of Industriousness are considered rather than scores on Conscientiousness. The SFPQ measure of Extraversion comprises three facet-level scales; namely, Dominance, Affiliation, and Exhibition. Dominance is defined by adjectives such as ‘‘assertive, authoritative, influential”, indicating that it would be categorized as an agentic trait. Affiliation, defined by terms such as ‘‘cooperative, friendly, neighbourly” falls under the category of communion. Exhibition, defined by adjectives such as ‘‘entertaining, flashy, dramatic” does not clearly fit under either agency or communion. In line with our theoretical categorization of the facet-level traits along agency/communion lines, Hough et al. (2001) reported gender differences for facets of Extraversion, where women scored higher on average than men on Affiliation, but lower than men on Dominance. Based on our theoretical argument, and the empirical findings of Hough et al. (2001), we put forth the following hypothesis regarding Extraversion: Hypothesis 2. There will more be evidence of differential selection by gender when scores on the agentic trait of Dominance and the communal trait of Affiliation are considered rather than scores on Extraversion.
1.3. Study hypotheses: FFM broad traits versus facet-level traits 2. Method We addressed our research question using the Six Factor Personality Questionnaire (SFPQ; Jackson, Paunonen, & Tremblay, 2000), which measures the FFM factors of Extraversion, Openness, Agreeableness, and Independence (reverse-keyed Neuroticism) and splits Conscientiousness into Industriousness and Methodicalness. Large-sample empirical results presented by Jackson et al. (2000)
2.1. Sample Participants were candidates (N = 572) applying for various positions at a large oil refinery in western Canada. There were 436 men and 136 women.
108
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
2.2. Materials Applicants were administered the Form L version (Jackson 1999a, 1999b) of scales from the Six Factor Personality Questionnaire (SFPQ; Jackson et al., 2000) as part of a larger selection battery. The scales that make up Extraversion (Affiliation, Dominance, and Exhibition) each have six items, and the constituent scales of Conscientiousness (Industriousness and Methodicalness) each have 18 items. All items were presented in 5-point Likert response format. Internal consistency reliability values for this study are presented in Table 1. The validity of the SFPQ has been substantiated in a number of studies (see Jackson et al., 2000, for a summary). 2.3. Procedure Although other selection tools were used by the organization, in the present study we focused on analyzing adverse impact of only the FFM broad traits of Extraversion and Conscientiousness and their facet-level traits. Applicants’ scores on each personality measure were rank-ordered separately. Although adverse impact can be assessed based on the outcome of a decision based on an entire selection system (combining multiple assessments), Collins and Morris (2008) argue that adverse impact can also be defined based on one particular component of a selection system. Thus, the adverse impact of each personality trait was calculated separately. Using a top-down strategy for each trait, we noted which applicants would be selected, using a range of selection ratios (the proportion of applicants selected; a selection ratio of .10 indicates that the top 10% of scorers would be selected.) Because this was a rapidly growing industry, large selection ratios were common in this context, and thus we have included selection ratios from .10 to .90. Once the top group of applicants was selected, we calculated the selection ratio for men and women separately. We then calculated the Adverse Impact Ratio (AIR) by dividing the female selection ratio by the male selection ratio. The respondents represented people who had applied for a variety of different technical positions in a large organization. Prior to data collection a formal job analysis had been completed for each of the substantive job families, with emphasis on identifying desired job-specific competencies (e.g., technical knowledge and skills) and desired job-general behaviours (e.g., effective teamwork). Personality traits were considered important predictors of the behavioural criteria deemed relevant across all jobs. Because this organization required a flexible, coordinated and adaptable workforce, it was critical to hire people who possessed attributes, like required personality traits, which would allow them to move within the organization and work well with others. Consequently, we analyzed the data from all entry-level respondents together. 3. Results Table 1 presents the means and standard deviations on the FFM broad traits and facet-level traits, as well as the effect sizes for the
mean differences between men and women (d-values). The means for the relevant norming sample from the SFPQ manual (1067 respondents from North America) are also presented for comparison purposes. The means for the current sample are slightly higher than the normative sample. This was expected given that the normative sample did not take the test under applicant conditions. For some of the facet-level traits, the d-values were moderate (e.g., .18 for Industriousness, .41 for Dominance, and .26 for Affiliation) which increases adverse impact potential. In selection contexts, the higher the d-value between groups on an assessment, the more that adverse impact would be expected for the lower scoring group. Tables 2 and 3 present the Adverse Impact Ratios (AIRs). The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired at each selection ratio. For any given trait, the size of the AIR depends upon the male/ female mean difference, but it is also strongly affected by the cut-off score, which is typically dictated by the selection ratio (Sackett & Wilk, 1994). Thus, as shown in Tables 2 and 3 the AIR was computed for each of several different selection ratios that are likely to occur in selection situations (.10 to .90). In addition to looking at AIRs, we also conducted z-tests to evaluate the null hypothesis of equal population selection rates for men and women (Collins & Morris, 2008). Hypothesis 1 predicted that there would be more evidence of adverse impact when using scores on the facet-level scale of Industriousness as compared to scores on Conscientiousness. Indeed, there was more evidence of adverse impact (3 out of 9 cases; Table 2) when using Industriousness as compared to FFM Conscientiousness (1 out of 9 cases). Hypothesis 2 predicted that there would be more evidence of adverse impact when scores on the facet-level scales of Extraversion (specifically Dominance and Affiliation) are considered rather than scores on Extraversion. Indeed, there was evidence of adverse impact in Dominance (in 8 out of 9 selection ratios) and Affiliation (3 out of 9 selection ratios, see Table 3). In contrast, there was just 1 case of adverse impact for Extraversion. Thus, when the facet-level scales of Extraversion are combined, adverse impact is reduced, providing support for Hypothesis 2. It should be noted that with completely normal distributions for both men and women, we would expect to find a particular point of violation – that is, a particular ‘‘threshold” selection ratio where mean differences translate into significant differences in selection rate. Once such a violation occurs, it should hold for all selection ratios that are smaller. However, in actual field data such as these, adverse impact ratios are not so predictable. For example, in Table 2, note that in the case of Industriousness, a significantly different selection rate occurs when the selection ratio is .40, but not when the selection ratio drops to .10. This is because the distributions of scores on each personality trait were not perfectly normal. That these applied data deviate from the ideal of perfect multivariate normality highlights the importance of collecting data in real organizational settings, where data may not conform to expected patterns as they would in Monte Carlo simulations.
Table 1 Means and standard deviations for FFM and facet-level traits.
Conscientiousness Industriousness Methodicalness Extraversion Affiliation Dominance Exhibition
a
Male Mean (SD)
Female Mean (SD)
.79 .66 .74 .84 .72 .86 .72
3.56 3.47 3.69 3.54 3.72 3.85 3.06
3.54 3.41 3.68 3.51 3.84 3.61 3.09
(.28) (.31) (.34) (.40) (.47) (.57) (.52)
(.29) (.33) (.34) (.43) (.48) (.65) (.51)
Effect Size (d) 0.13 0.18 0.03 0.08 0.26 0.41 0.06
Male Norms (SD)
Female Norms (SD)
3.39 3.38 3.40 3.19 3.21 3.33 3.02
3.38 3.33 3.44 3.11 3.38 3.02 2.93
(.40) (.45) (.51) (.54) (.63) (.71) (.68)
(.41) (.44) (.53) (.58) (.67) (.77) (.73)
Norm Sample Effect Size (d) 0.02 0.11 0.08 0.14 0.26 0.42 0.13
Note. Effect size (d) refers to the standardized difference between groups; a positive value indicates a larger mean for men. Norms refer to the mean values reported from the SFPQ manual (1067 respondents from North America). Norm sample effect size refers to the standardized difference between groups based on the normative sample means.
109
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110 Table 2 Adverse impact ratios for conscientiousness and facet-level traits at varying selection ratios. SR
FFM trait
Facet-level traits
Conscientiousness
.90 .80 .70 .60 .50 .40 .30 .20 .10
Industriousness
Methodicalness
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
0.96 1.00 0.99 0.92 0.86 0.81 0.71 0.68 0.86
1.14 0.01 0.09 0.99 1.50 1.63 2.06* 1.74 0.50
0.93 0.99 0.95 0.95 0.88 0.77 0.77 0.60 1.05
2.13* 0.23 0.74 0.58 1.30 2.03* 1.63 2.23* 0.16
0.98 1.01 1.05 1.05 0.95 0.95 1.02 0.86 0.86
0.50 0.23 0.74 0.58 0.52 0.38 0.12 0.73 0.49
Cases of unequal selection rates
1/9
3/9
0/9
Note. AIR, adverse impact ratio. The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired. The z-test evaluates the null hypothesis of equal population selection rates for men and women. * Indicates z is significant at the .05 level. Table 3 Adverse impact ratios for extraversion and facet-level traits at varying selection ratios. SR
FFM trait
Facet-level traits
Extraversion
.90 .80 .70 .60 .50 .40 .30 .20 .10 Cases of unequal selection rates
Affiliation
Dominance
Exhibition
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
0.98 0.95 0.88 0.84 0.92 0.87 0.93 1.01 1.38
0.53 1.04 1.90 2.10* 0.79 1.13 0.47 0.05 1.19
0.99 1.06 1.12 1.20 1.11 1.23 1.26 1.55 1.74
0.16 1.23 1.83 2.41* 1.08 1.81 1.61 2.46* 2.13*
0.92 0.84 0.78 0.67 0.72 0.57 0.55 0.60 0.61
2.47* 3.46* 3.56* 4.44* 3.08* 4.00* 3.32* 2.20* 1.47
1.02 1.00 1.01 1.08 1.13 1.10 1.05 1.10 1.15
0.49 0.00 0.11 1.01 1.28 0.80 0.32 0.49 0.49
1/9
3/9
8/9
0/9
Note. AIR, adverse impact ratio. The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired. The z-test evaluates the null hypothesis of equal population selection rates for men and women. * Indicates z is significant at the .05 level.
4. Discussion One of the often cited benefits of adding personality measures to a selection battery is that personality measures are reported to have little, if any, adverse impact on protected groups (Hough, 1997). Indeed, the findings presented in this study do support the notion that FFM personality measures show minimal group differences. However, it would be incorrect to conclude that differences on personality measures do not exist at all. Even when effects sizes were relatively small (e.g., d = .41 for Dominance), there was evidence of differential selection rates at most of the selection ratios. We explored this apparent contradiction by investigating whether differences in some facet-level personality traits may be cancelled out at the FFM level. We expected to find gender differences on facet-level personality traits that could be categorized as agentic or communal. In contrast, FFM broad traits combine both agentic and communal traits (Wiggins & Trapnell, 1996) and thus we expected that gender differences would be reduced at the FFM level. We investigated the effect on adverse impact of combining the facet-level scales of Extraversion and Conscientiousness into their respective FFM broad traits. In the case of Extraversion, the facet of Dominance did show evidence of adverse impact against women, whereas the facet of Affiliation showed adverse impact against men. However, when Dominance and Affiliation were combined with Exhibition to form the FFM broad trait of Extraversion, there was minimal evidence of adverse impact. There was also a significant reduction in the cases of adverse impact when Industriousness, which did engender adverse impact, was combined with Methodicalness,
which did not cause adverse impact, to form the FFM broad trait Conscientiousness. In general, we found that when a facet-level trait was reflective of agency or communion, the effect on adverse impact was reduced by combining it with other facet-level traits. Combining these facet-level traits together, through the use of FFM broad traits, may be one way to mitigate the potential for adverse impact in a selection context. Because male/female mean differences in facet-level traits appear to be prevalent, many test publishers have gender-specific norms to score personality measures. However, gender-specific norms are generally not used when using personality measures to make employment-related decisions (Saad & Sackett, 2002). In the United States, for example, Section 106 of the Civil Rights Act of 1991 prohibits score adjustments as a way of dealing with these differences. This causes a social dilemma that, our results suggest, may generally be somewhat lessened by using FFM broad traits rather than facet-level traits. Our results also add further relevant considerations to the decision of whether to use facet-level traits or FFM traits in personnel selection. Some research suggests that there are predictive advantages of using facet-level traits over FFM broad traits. For example, Mount and Barrick (1995) compared the validity of the FFM broad trait of Conscientiousness to the validities of the facet-level traits of Achievement and Dependability. They classified the criteria as representing either overall job performance, or a specific component of performance (e.g., effort, quality, creativity). They found that Achievement was the best predictor of some specific performance measures (e.g., creativity), whereas another facet-level trait, Dependability, was uncorrelated with creativity but was the best
110
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
predictor of quality. Their findings suggest that facet-level traits may be better predictors of specific job performance criteria. Rothstein and Jelley (2003) argued that using facet-level traits may provide more insight as to why a particular trait is relevant to a particular component of job performance. However, Mount and Barrick (1995) also stated that the FFM broad traits were effective for predicting overall job proficiency. In an applied pre-employment testing situation, the potential predictive and interpretive advantages must be considered in light of adverse impact concerns that we have raised. In addition to combining facet-level traits to form FFM broad traits, it is also conceivable that adverse impact associated with the use of certain facet-level traits might be lessened by combining personality testing with other selection methods, rather than using them in isolation. Pulakos and Schmitt (1996) found that the strategy of adding a variety of job-relevant measures (e.g., structured interview) to a written verbal ability measure, which on its own caused adverse impact, added incremental validity in the prediction of performance, and also reduced adverse impact. In situations where facet-level agentic or communal personality traits are used, it may be possible to include other valid measures, which are uncorrelated with personality, to reduce the potential adverse impact of using the facet-level traits alone. It is important to be aware of the extent to which each specific selection method, such as personality testing, may contribute to adverse impact so that appropriate combinations of selection methods might be derived (e.g., Schmitt, Rogers, Chan, Sheppard, & Jennings, 1997).
4.1. Limitations and future directions Our conclusions are made on the basis of a single study, so the results cannot be viewed as definitive. However, our results do make an important contribution because the personality measures were administered in an actual selection context, and the male/ female differences in our sample provided insights that could not have been gleaned from the test manual or from computer simulations. Because there have been interpretive advantages cited for using facet-level measures (e.g., Rothstein & Jelley, 2003), further research is needed to understand how to optimize pre-employment personality testing to maximize prediction and interpretability, while minimizing adverse impact against protected groups. In addition, future research could involve different work contexts and different personality traits than were used in this study.
4.2. Conclusions In summary, we found that when a facet-level personality measure is reflective of agency or communion, its use in a selection context may lead to adverse impact. However, the adverse impact can be reduced by combining that trait with other, non agencyrelated or non-communion-related, traits. Indeed, the use of the FFM may be one way to mitigate the potential for differential hiring rates by gender in a selection context.
References Bakan, D. (1966). The duality of human existence. Boston: Beacon Press. Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance. A meta-analysis. Personnel Psychology, 44, 1–26. Barrick, M. R., Mount, M. K., & Judge, T. A. (2001). Personality and performance at the beginning of the new millennium: What do we know and where do we go next? International Journal of Selection and Assessment, 9, 9–30. Collins, M. W., & Morris, S. B. (2008). Testing for adverse impact when sample size is small. Journal of Applied Psychology, 93, 463–471. Costa, P. J., Jr., & McCrae, R. R. (1992). Revised NEO Personality Inventory Professional Manual. Odessa, FL: Psychological Assessment Resources, Inc. Feingold, A. (1994). Gender differences in personality: A meta-analysis. Psychological Bulletin, 116, 429–456. Foldes, H. J., Duehr, E. E., & Ones, D. S. (2008). Group differences in personality: Meta-analysis comparing five U.S Racial groups. Personnel Psychology, 61, 579–616. Hogan, R., & Shelton, D. (1998). A socioanalytic perspective on job performance. Human Performance, 11, 129–144. Hough, L. M. (1997). The millennium for personality psychology: new horizon or good old daze. Applied Psychology: An International Review, 47, 233–261. Hough, L. M., Oswald, F. L., & Ployhart, R. E. (2001). Determinants, detection and amelioration of adverse impact in personnel selection procedures: Issues, evidence and lessons learned. International Journal of Selection and Assessment, 9, 152–194. Jackson, D. N. (1999a). Jackson personality inventory Form L. Port Huron, MI: Sigma Assessment Systems. Jackson, D. N. (1999b). Personality research form, Form L. Port Huron, MI: Sigma Assessment Systems. Jackson, D. N., Paunonen, S. V., Fraboni, M., & Goffin, R. D. (1996). A five-factor versus six-factor model of personality structure. Personality and Individual Differences, 20, 33–45. Jackson, D. N., Paunonen, S. V., & Tremblay, P. F. (2000). Six factor personality questionnaire manual. Port Huron, MI: Sigma Assessment Systems. Mount, M. K., & Barrick, M. R. (1995). The Big Five personality dimensions: Implications for research and practice in human resource management. In G. Ferris (Ed.). Research in Personnel and Human Resource Management (Vol. 13, pp. 153–200). Stanford, CT: JAI. Ones, D. S., & Anderson, N. (2002). Gender and ethnic differences on personality scales in selection: Some British data. Journal of Occupational and Organizational Psychology, 75, 255–276. Pulakos, E. D., & Schmitt, N. (1996). An evaluation of two strategies for reducing adverse impact and their effects on criterion-related validity. Human Performance, 9, 241–258. Rothstein, M. G., & Jelley, R. B. (2003). The challenge of aggregating studies of personality. In K. R. Murphy (Ed.), Validity generalization (pp. 223–262). Mahwah, NJ: Lawrence Erlbaum Associates. Saad, S., & Sackett, P. R. (2002). Investigating differential prediction by gender in employment-oriented personality measures. Journal of Applied Psychology, 87, 667–674. Sackett, P. R., & Wilk, S. L. (1994). Within-group norming and other forms of adjustment in preemployment testing. American Psychologist, 49, 929–954. Saucier, G., & Goldberg, L. R. (2003). The structure of personality attributes. In M. R. Barrick & A. M. Ryan (Eds.), Personality and work (pp. 1–29). San Francisco: Jossey-Bass. Schmitt, N., Rogers, W., Chan, D., Sheppard, L., & Jennings, D. (1997). Adverse impact and predictive efficiency of various predictor combinations. Journal of Applied Psychology, 82, 719–730. Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality measures as predictors of job performance. A meta-analytic review. Personnel Psychology, 44, 703–742. Uniform Guidelines on Employee Selection (1978). Federal Register, 43, 38290–38315. Wiggins, J. S. (1991). Agency and communion as conceptual coordinates for the understanding and measurement of interpersonal behaviour. In W. M. Grove & D. Cicchetti (Eds.). Thinking clearly about psychology (Vol. 2). Minneapolis: University of Minnesota Press. Wiggins, J. S., & Trapnell, P. D. (1996). A dyadic-interactional perspective on the fivefactor model. In J. S. Wiggins (Ed.), The five-factor model of personality: Theoretical perspectives (pp. 88–162). New York: Guilford. Women and Work Commission (2006). Shaping a fairer future. London: Women and Work Commission.
Contents lists available at ScienceDirect
Personality and Individual Differences journal homepage: www.elsevier.com/locate/paid
Gender differences in personality scores: Implications for differential hiring rates Deborah M. Powell a,⇑, Richard D. Goffin b, Ian R. Gellatly c a
Department of Psychology, University of Guelph, Guelph, Ontario, Canada N1G 2W1 Department of Psychology, The University of Western Ontario, London, Ontario, Canada N6A 5C2 c School of Business, University of Alberta, Edmonton, Canada T6G 2R6 b
a r t i c l e
i n f o
Article history: Received 21 June 2010 Received in revised form 2 September 2010 Accepted 7 September 2010
Keywords: Personnel selection Adverse impact Five Factor Model Personality testing
a b s t r a c t We investigated the extent to which gender differences in personality test scores used in a personnel selection context are likely to cause differential hiring rates. Participants were candidates (N = 572) applying for positions at an oil refinery. Candidates completed both facet-level and broad Five Factor Model (FFM) personality measures. Focusing on the FFM broad traits of Extraversion and Conscientiousness, we found that generally there was less adverse impact when using FFM broad traits as compared to facet-level traits. When facet-level traits were reflective of agency – an underlying need to be autonomous, or communion – a need to be part of a larger social entity, they led to differential hiring rates for men or women. This problem was greatly reduced when using the FFM broad traits. When used for personnel selection, the use of FFM broad traits may help to promote gender diversity in the workplace. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction A recent report by the UK Women and Work Commission (2006) in February, discussed the problem of ‘occupational segregation’, that is, where one gender predominates in a particular segment of the workforce. This segregation can be problematic if, for example, a concentration of women in lower paying occupations contributes to an overall gender pay gap, or if excluding one gender from a particular occupation inadvertently creates labour shortages. One way to address this problem of occupational segregation, and therefore promote gender diversity, is through examining personnel selection tools to determine if the way that job candidates are selected facilitates gender diversity. Some personnel selection tools, such spatial ability tests, or tests of physical strength, show evidence of gender differences in scores. When one group scores lower, on average, on a given selection tool, that group is likely to be selected at a lower rate, thus creating barriers to organizational entry for the lower scoring group. When the selection rate for a protected group is lower than that for the relevant comparison group, adverse impact is said to have occurred (Uniform Guidelines on Employee Selection, 1978). Personality measures are often cited as selection tools that do not cause differential selection rates for protected groups (Hough, 1997) yet still show evidence of criterion-related validity (Barrick & Mount, 1991; Tett, Jackson, & Rothstein, 1991). Despite the claim that personality measures do not cause adverse impact, ⇑ Corresponding author. Tel.: +1 519 824 4120x52167; fax: +1 519 837 8629. E-mail addresses: [email protected] (D.M. Powell), goffi[email protected] (R.D. Goffin), [email protected] (I.R. Gellatly). 0191-8869/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2010.09.010
demographic group differences in personality traits do exist. Several researchers have documented gender differences in personality traits, particularly at the facet level (e.g., Feingold, 1994; Hough, Oswald, & Ployhart, 2001). In contrast, when reported at the broad Five Factor Model (FFM) level, gender differences are generally reported to be negligible (e.g., Ones & Anderson, 2002). One issue that has yet to be fully addressed by research is the extent to which gender differences in personality scores are likely to cause differential hiring rates in personnel selection, and whether the use of facet-level traits versus FFM broad traits affects differential hiring rates. It is this issue that we address in the paper. 1.1. Theoretical framework: agency and communion Bakan (1966) theoretical framework of agency and communion may help in the explication of the basis of gender differences in personality measures. Wiggins (1991) described agency as ‘‘the condition of being a differentiated individual and it is evident in strivings for mastery and power which enhance and protect that differentiation” (p. 89). In contrast, communion is described as ‘‘the condition of being part of a larger social or spiritual entity, and it is manifested in strivings for intimacy, union, and solidarity with that larger entity” (p. 89). Agency and communion were proposed as underlying human needs. Social scientists have proposed that the underlying needs of agency and communion may be reflected in corresponding personality traits that facilitate the satisfaction of those needs (Wiggins, 1991; Wiggins & Trapnell, 1996). Indeed, Saucier and Goldberg (2003) noted that in lexical studies of personality, a two-factor structure is commonly found that is consistent with Bakan’s theory.
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
These two factors have also been called ‘‘getting ahead” and ‘‘getting along” by Hogan and Shelton (1998). Wiggins (1991) argued that historically, the concepts of agency and communion have been associated with men and women, respectively. Based on this framework, we would predict that women would score lower on measures of agentic traits, and such traits might therefore cause adverse impact if used in a selection context, and that men would score lower on communal traits, which would cause adverse impact for men. Wiggins and Trapnell (1996) argued that manifestations of both agency and communion can be identified within several of the FFM factors. For example, a facet-level trait such as Achievement Striving, which is part of Conscientiousness, can be categorized as an agentic facet, whereas other Conscientiousness facets (e.g., Dutifulness) can be categorized as communal. Although many facets can be categorized into agency or communion, the interpretation of FFM factors as agentic or communal is less clear. Based on the argument that both agentic and communal facet-level traits make up the FFM broad traits, we would predict that when assessed as FFM broad traits, personality measures will not lead to gender differences. Previous work has not investigated whether the use of FFM broad traits versus facet-level traits in a selection context would have implications for differential hiring rates of men and women. This study makes a unique contribution because we explore the use of facet-level agentic or communal traits versus FFM broad traits, in terms of the effect on differential selection of women and men in an applied selection scenario. Previous research (e.g., Hough et al., 2001) has looked primarily at mean differences between men and women. In this study, we investigated whether mean differences would translate into meaningful differences in hiring rates in an actual selection context. In the following sections, we describe research suggesting that gender differences may be reduced when using FFM broad traits, and we outline our study hypotheses.
107
indicate that the SFPQ is representative of other popular FFM measures such as the NEO-PI-R (Costa & McCrae, 1992), with correlations between corresponding factors ranging from .22 (NEO Neuroticism and SFPQ Independence) to .71 (NEO Extraversion and SFPQ Extraversion). Thus, results from the SFPQ can be expected to generalize to other FFM measures. Based on Hough et al. (2001) findings regarding gender differences in the facets of Extraversion and Conscientiousness specifically, we focused only on these two factors of the FFM. Moreover, although our study was conducted in one specific workplace, Barrick, Mount, and Judge (2001) reported that Conscientiousness is a valid predictor of performance across a variety of criterion types and occupational groups, and also reported that Extraversion was a strong predictor of success in training. Because our study was conducted in a high-tech facility, constant training was required to keep up with new technology. In fact, compensation was directly linked to skills updating, and thus Extraversion would be important for success in this organization. As well, Barrick and Mount (1991) meta-analysis found that, among FFM factors, Conscientiousness and Extraversion were, overall, the two strongest predictors of job-relevant criteria. With respect to the FFM trait of Conscientiousness, the SFPQ, in consonance with empirical findings (Jackson, Paunonen, Fraboni, & Goffin, 1996), provides two main constituent scales; namely, Industriousness and Methodicalness. Industriousness, defined by terms such as ‘‘striving, determined, and unrelenting in work habits”, is a trait that seems consistent with agency. Methodicalness, defined by terms such as ‘‘organized, neat, and thinks before acting” likely does not fall clearly under either agency or communion. Hough et al. (2001) reported that men tend to score higher on Achievement (similar to Industriousness). Based on empirical findings of Hough et al. (2001), which are consistent with our theoretical argument that Industriousness is an agentic trait whereas Methodicalness is not, we propose the following hypothesis:
1.2. Evidence of gender differences When reported at the FFM level, male/female differences in personality traits have generally been found to be negligible. For example, Ones and Anderson (2002) reported no large gender differences across three different FFM personality inventories in a large sample of British University students. Hough et al. (2001), however, reported that a difference did appear in one of the facet-level traits of Conscientiousness; women tended to score higher than men on Dependability but about the same as men on Achievement. Hough et al. (2001) also reported differences in the facets that make up Extraversion; women scored lower on average than men on Dominance but higher than men on Affiliation – a finding that is consistent with the proposition that women tend to score lower on agentic traits. It seems that examining traits at the facet level could potentially reveal gender differences that cancel out at the FFM level, at least for the factors of Extraversion and Conscientiousness. In a similar vein, a meta-analysis found that a more complete picture of racial subgroup differences was evident from analyses conducted at the facet level as opposed to the FFM level (Foldes, Duehr, & Ones, 2008). Facet-level differences in the mean scores of different groups may be averaged out at the FFM level, meaning that although group differences may exist, they are less apparent when traits are measured as FFM broad traits.
Hypothesis 1. There will be more evidence of differential selection by gender when scores on the agentic trait of Industriousness are considered rather than scores on Conscientiousness. The SFPQ measure of Extraversion comprises three facet-level scales; namely, Dominance, Affiliation, and Exhibition. Dominance is defined by adjectives such as ‘‘assertive, authoritative, influential”, indicating that it would be categorized as an agentic trait. Affiliation, defined by terms such as ‘‘cooperative, friendly, neighbourly” falls under the category of communion. Exhibition, defined by adjectives such as ‘‘entertaining, flashy, dramatic” does not clearly fit under either agency or communion. In line with our theoretical categorization of the facet-level traits along agency/communion lines, Hough et al. (2001) reported gender differences for facets of Extraversion, where women scored higher on average than men on Affiliation, but lower than men on Dominance. Based on our theoretical argument, and the empirical findings of Hough et al. (2001), we put forth the following hypothesis regarding Extraversion: Hypothesis 2. There will more be evidence of differential selection by gender when scores on the agentic trait of Dominance and the communal trait of Affiliation are considered rather than scores on Extraversion.
1.3. Study hypotheses: FFM broad traits versus facet-level traits 2. Method We addressed our research question using the Six Factor Personality Questionnaire (SFPQ; Jackson, Paunonen, & Tremblay, 2000), which measures the FFM factors of Extraversion, Openness, Agreeableness, and Independence (reverse-keyed Neuroticism) and splits Conscientiousness into Industriousness and Methodicalness. Large-sample empirical results presented by Jackson et al. (2000)
2.1. Sample Participants were candidates (N = 572) applying for various positions at a large oil refinery in western Canada. There were 436 men and 136 women.
108
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
2.2. Materials Applicants were administered the Form L version (Jackson 1999a, 1999b) of scales from the Six Factor Personality Questionnaire (SFPQ; Jackson et al., 2000) as part of a larger selection battery. The scales that make up Extraversion (Affiliation, Dominance, and Exhibition) each have six items, and the constituent scales of Conscientiousness (Industriousness and Methodicalness) each have 18 items. All items were presented in 5-point Likert response format. Internal consistency reliability values for this study are presented in Table 1. The validity of the SFPQ has been substantiated in a number of studies (see Jackson et al., 2000, for a summary). 2.3. Procedure Although other selection tools were used by the organization, in the present study we focused on analyzing adverse impact of only the FFM broad traits of Extraversion and Conscientiousness and their facet-level traits. Applicants’ scores on each personality measure were rank-ordered separately. Although adverse impact can be assessed based on the outcome of a decision based on an entire selection system (combining multiple assessments), Collins and Morris (2008) argue that adverse impact can also be defined based on one particular component of a selection system. Thus, the adverse impact of each personality trait was calculated separately. Using a top-down strategy for each trait, we noted which applicants would be selected, using a range of selection ratios (the proportion of applicants selected; a selection ratio of .10 indicates that the top 10% of scorers would be selected.) Because this was a rapidly growing industry, large selection ratios were common in this context, and thus we have included selection ratios from .10 to .90. Once the top group of applicants was selected, we calculated the selection ratio for men and women separately. We then calculated the Adverse Impact Ratio (AIR) by dividing the female selection ratio by the male selection ratio. The respondents represented people who had applied for a variety of different technical positions in a large organization. Prior to data collection a formal job analysis had been completed for each of the substantive job families, with emphasis on identifying desired job-specific competencies (e.g., technical knowledge and skills) and desired job-general behaviours (e.g., effective teamwork). Personality traits were considered important predictors of the behavioural criteria deemed relevant across all jobs. Because this organization required a flexible, coordinated and adaptable workforce, it was critical to hire people who possessed attributes, like required personality traits, which would allow them to move within the organization and work well with others. Consequently, we analyzed the data from all entry-level respondents together. 3. Results Table 1 presents the means and standard deviations on the FFM broad traits and facet-level traits, as well as the effect sizes for the
mean differences between men and women (d-values). The means for the relevant norming sample from the SFPQ manual (1067 respondents from North America) are also presented for comparison purposes. The means for the current sample are slightly higher than the normative sample. This was expected given that the normative sample did not take the test under applicant conditions. For some of the facet-level traits, the d-values were moderate (e.g., .18 for Industriousness, .41 for Dominance, and .26 for Affiliation) which increases adverse impact potential. In selection contexts, the higher the d-value between groups on an assessment, the more that adverse impact would be expected for the lower scoring group. Tables 2 and 3 present the Adverse Impact Ratios (AIRs). The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired at each selection ratio. For any given trait, the size of the AIR depends upon the male/ female mean difference, but it is also strongly affected by the cut-off score, which is typically dictated by the selection ratio (Sackett & Wilk, 1994). Thus, as shown in Tables 2 and 3 the AIR was computed for each of several different selection ratios that are likely to occur in selection situations (.10 to .90). In addition to looking at AIRs, we also conducted z-tests to evaluate the null hypothesis of equal population selection rates for men and women (Collins & Morris, 2008). Hypothesis 1 predicted that there would be more evidence of adverse impact when using scores on the facet-level scale of Industriousness as compared to scores on Conscientiousness. Indeed, there was more evidence of adverse impact (3 out of 9 cases; Table 2) when using Industriousness as compared to FFM Conscientiousness (1 out of 9 cases). Hypothesis 2 predicted that there would be more evidence of adverse impact when scores on the facet-level scales of Extraversion (specifically Dominance and Affiliation) are considered rather than scores on Extraversion. Indeed, there was evidence of adverse impact in Dominance (in 8 out of 9 selection ratios) and Affiliation (3 out of 9 selection ratios, see Table 3). In contrast, there was just 1 case of adverse impact for Extraversion. Thus, when the facet-level scales of Extraversion are combined, adverse impact is reduced, providing support for Hypothesis 2. It should be noted that with completely normal distributions for both men and women, we would expect to find a particular point of violation – that is, a particular ‘‘threshold” selection ratio where mean differences translate into significant differences in selection rate. Once such a violation occurs, it should hold for all selection ratios that are smaller. However, in actual field data such as these, adverse impact ratios are not so predictable. For example, in Table 2, note that in the case of Industriousness, a significantly different selection rate occurs when the selection ratio is .40, but not when the selection ratio drops to .10. This is because the distributions of scores on each personality trait were not perfectly normal. That these applied data deviate from the ideal of perfect multivariate normality highlights the importance of collecting data in real organizational settings, where data may not conform to expected patterns as they would in Monte Carlo simulations.
Table 1 Means and standard deviations for FFM and facet-level traits.
Conscientiousness Industriousness Methodicalness Extraversion Affiliation Dominance Exhibition
a
Male Mean (SD)
Female Mean (SD)
.79 .66 .74 .84 .72 .86 .72
3.56 3.47 3.69 3.54 3.72 3.85 3.06
3.54 3.41 3.68 3.51 3.84 3.61 3.09
(.28) (.31) (.34) (.40) (.47) (.57) (.52)
(.29) (.33) (.34) (.43) (.48) (.65) (.51)
Effect Size (d) 0.13 0.18 0.03 0.08 0.26 0.41 0.06
Male Norms (SD)
Female Norms (SD)
3.39 3.38 3.40 3.19 3.21 3.33 3.02
3.38 3.33 3.44 3.11 3.38 3.02 2.93
(.40) (.45) (.51) (.54) (.63) (.71) (.68)
(.41) (.44) (.53) (.58) (.67) (.77) (.73)
Norm Sample Effect Size (d) 0.02 0.11 0.08 0.14 0.26 0.42 0.13
Note. Effect size (d) refers to the standardized difference between groups; a positive value indicates a larger mean for men. Norms refer to the mean values reported from the SFPQ manual (1067 respondents from North America). Norm sample effect size refers to the standardized difference between groups based on the normative sample means.
109
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110 Table 2 Adverse impact ratios for conscientiousness and facet-level traits at varying selection ratios. SR
FFM trait
Facet-level traits
Conscientiousness
.90 .80 .70 .60 .50 .40 .30 .20 .10
Industriousness
Methodicalness
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
0.96 1.00 0.99 0.92 0.86 0.81 0.71 0.68 0.86
1.14 0.01 0.09 0.99 1.50 1.63 2.06* 1.74 0.50
0.93 0.99 0.95 0.95 0.88 0.77 0.77 0.60 1.05
2.13* 0.23 0.74 0.58 1.30 2.03* 1.63 2.23* 0.16
0.98 1.01 1.05 1.05 0.95 0.95 1.02 0.86 0.86
0.50 0.23 0.74 0.58 0.52 0.38 0.12 0.73 0.49
Cases of unequal selection rates
1/9
3/9
0/9
Note. AIR, adverse impact ratio. The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired. The z-test evaluates the null hypothesis of equal population selection rates for men and women. * Indicates z is significant at the .05 level. Table 3 Adverse impact ratios for extraversion and facet-level traits at varying selection ratios. SR
FFM trait
Facet-level traits
Extraversion
.90 .80 .70 .60 .50 .40 .30 .20 .10 Cases of unequal selection rates
Affiliation
Dominance
Exhibition
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
AIR (f/m)
z
0.98 0.95 0.88 0.84 0.92 0.87 0.93 1.01 1.38
0.53 1.04 1.90 2.10* 0.79 1.13 0.47 0.05 1.19
0.99 1.06 1.12 1.20 1.11 1.23 1.26 1.55 1.74
0.16 1.23 1.83 2.41* 1.08 1.81 1.61 2.46* 2.13*
0.92 0.84 0.78 0.67 0.72 0.57 0.55 0.60 0.61
2.47* 3.46* 3.56* 4.44* 3.08* 4.00* 3.32* 2.20* 1.47
1.02 1.00 1.01 1.08 1.13 1.10 1.05 1.10 1.15
0.49 0.00 0.11 1.01 1.28 0.80 0.32 0.49 0.49
1/9
3/9
8/9
0/9
Note. AIR, adverse impact ratio. The AIR was computed by dividing the percentage of women who would be hired by the percentage of men who would be hired. The z-test evaluates the null hypothesis of equal population selection rates for men and women. * Indicates z is significant at the .05 level.
4. Discussion One of the often cited benefits of adding personality measures to a selection battery is that personality measures are reported to have little, if any, adverse impact on protected groups (Hough, 1997). Indeed, the findings presented in this study do support the notion that FFM personality measures show minimal group differences. However, it would be incorrect to conclude that differences on personality measures do not exist at all. Even when effects sizes were relatively small (e.g., d = .41 for Dominance), there was evidence of differential selection rates at most of the selection ratios. We explored this apparent contradiction by investigating whether differences in some facet-level personality traits may be cancelled out at the FFM level. We expected to find gender differences on facet-level personality traits that could be categorized as agentic or communal. In contrast, FFM broad traits combine both agentic and communal traits (Wiggins & Trapnell, 1996) and thus we expected that gender differences would be reduced at the FFM level. We investigated the effect on adverse impact of combining the facet-level scales of Extraversion and Conscientiousness into their respective FFM broad traits. In the case of Extraversion, the facet of Dominance did show evidence of adverse impact against women, whereas the facet of Affiliation showed adverse impact against men. However, when Dominance and Affiliation were combined with Exhibition to form the FFM broad trait of Extraversion, there was minimal evidence of adverse impact. There was also a significant reduction in the cases of adverse impact when Industriousness, which did engender adverse impact, was combined with Methodicalness,
which did not cause adverse impact, to form the FFM broad trait Conscientiousness. In general, we found that when a facet-level trait was reflective of agency or communion, the effect on adverse impact was reduced by combining it with other facet-level traits. Combining these facet-level traits together, through the use of FFM broad traits, may be one way to mitigate the potential for adverse impact in a selection context. Because male/female mean differences in facet-level traits appear to be prevalent, many test publishers have gender-specific norms to score personality measures. However, gender-specific norms are generally not used when using personality measures to make employment-related decisions (Saad & Sackett, 2002). In the United States, for example, Section 106 of the Civil Rights Act of 1991 prohibits score adjustments as a way of dealing with these differences. This causes a social dilemma that, our results suggest, may generally be somewhat lessened by using FFM broad traits rather than facet-level traits. Our results also add further relevant considerations to the decision of whether to use facet-level traits or FFM traits in personnel selection. Some research suggests that there are predictive advantages of using facet-level traits over FFM broad traits. For example, Mount and Barrick (1995) compared the validity of the FFM broad trait of Conscientiousness to the validities of the facet-level traits of Achievement and Dependability. They classified the criteria as representing either overall job performance, or a specific component of performance (e.g., effort, quality, creativity). They found that Achievement was the best predictor of some specific performance measures (e.g., creativity), whereas another facet-level trait, Dependability, was uncorrelated with creativity but was the best
110
D.M. Powell et al. / Personality and Individual Differences 50 (2011) 106–110
predictor of quality. Their findings suggest that facet-level traits may be better predictors of specific job performance criteria. Rothstein and Jelley (2003) argued that using facet-level traits may provide more insight as to why a particular trait is relevant to a particular component of job performance. However, Mount and Barrick (1995) also stated that the FFM broad traits were effective for predicting overall job proficiency. In an applied pre-employment testing situation, the potential predictive and interpretive advantages must be considered in light of adverse impact concerns that we have raised. In addition to combining facet-level traits to form FFM broad traits, it is also conceivable that adverse impact associated with the use of certain facet-level traits might be lessened by combining personality testing with other selection methods, rather than using them in isolation. Pulakos and Schmitt (1996) found that the strategy of adding a variety of job-relevant measures (e.g., structured interview) to a written verbal ability measure, which on its own caused adverse impact, added incremental validity in the prediction of performance, and also reduced adverse impact. In situations where facet-level agentic or communal personality traits are used, it may be possible to include other valid measures, which are uncorrelated with personality, to reduce the potential adverse impact of using the facet-level traits alone. It is important to be aware of the extent to which each specific selection method, such as personality testing, may contribute to adverse impact so that appropriate combinations of selection methods might be derived (e.g., Schmitt, Rogers, Chan, Sheppard, & Jennings, 1997).
4.1. Limitations and future directions Our conclusions are made on the basis of a single study, so the results cannot be viewed as definitive. However, our results do make an important contribution because the personality measures were administered in an actual selection context, and the male/ female differences in our sample provided insights that could not have been gleaned from the test manual or from computer simulations. Because there have been interpretive advantages cited for using facet-level measures (e.g., Rothstein & Jelley, 2003), further research is needed to understand how to optimize pre-employment personality testing to maximize prediction and interpretability, while minimizing adverse impact against protected groups. In addition, future research could involve different work contexts and different personality traits than were used in this study.
4.2. Conclusions In summary, we found that when a facet-level personality measure is reflective of agency or communion, its use in a selection context may lead to adverse impact. However, the adverse impact can be reduced by combining that trait with other, non agencyrelated or non-communion-related, traits. Indeed, the use of the FFM may be one way to mitigate the potential for differential hiring rates by gender in a selection context.
References Bakan, D. (1966). The duality of human existence. Boston: Beacon Press. Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance. A meta-analysis. Personnel Psychology, 44, 1–26. Barrick, M. R., Mount, M. K., & Judge, T. A. (2001). Personality and performance at the beginning of the new millennium: What do we know and where do we go next? International Journal of Selection and Assessment, 9, 9–30. Collins, M. W., & Morris, S. B. (2008). Testing for adverse impact when sample size is small. Journal of Applied Psychology, 93, 463–471. Costa, P. J., Jr., & McCrae, R. R. (1992). Revised NEO Personality Inventory Professional Manual. Odessa, FL: Psychological Assessment Resources, Inc. Feingold, A. (1994). Gender differences in personality: A meta-analysis. Psychological Bulletin, 116, 429–456. Foldes, H. J., Duehr, E. E., & Ones, D. S. (2008). Group differences in personality: Meta-analysis comparing five U.S Racial groups. Personnel Psychology, 61, 579–616. Hogan, R., & Shelton, D. (1998). A socioanalytic perspective on job performance. Human Performance, 11, 129–144. Hough, L. M. (1997). The millennium for personality psychology: new horizon or good old daze. Applied Psychology: An International Review, 47, 233–261. Hough, L. M., Oswald, F. L., & Ployhart, R. E. (2001). Determinants, detection and amelioration of adverse impact in personnel selection procedures: Issues, evidence and lessons learned. International Journal of Selection and Assessment, 9, 152–194. Jackson, D. N. (1999a). Jackson personality inventory Form L. Port Huron, MI: Sigma Assessment Systems. Jackson, D. N. (1999b). Personality research form, Form L. Port Huron, MI: Sigma Assessment Systems. Jackson, D. N., Paunonen, S. V., Fraboni, M., & Goffin, R. D. (1996). A five-factor versus six-factor model of personality structure. Personality and Individual Differences, 20, 33–45. Jackson, D. N., Paunonen, S. V., & Tremblay, P. F. (2000). Six factor personality questionnaire manual. Port Huron, MI: Sigma Assessment Systems. Mount, M. K., & Barrick, M. R. (1995). The Big Five personality dimensions: Implications for research and practice in human resource management. In G. Ferris (Ed.). Research in Personnel and Human Resource Management (Vol. 13, pp. 153–200). Stanford, CT: JAI. Ones, D. S., & Anderson, N. (2002). Gender and ethnic differences on personality scales in selection: Some British data. Journal of Occupational and Organizational Psychology, 75, 255–276. Pulakos, E. D., & Schmitt, N. (1996). An evaluation of two strategies for reducing adverse impact and their effects on criterion-related validity. Human Performance, 9, 241–258. Rothstein, M. G., & Jelley, R. B. (2003). The challenge of aggregating studies of personality. In K. R. Murphy (Ed.), Validity generalization (pp. 223–262). Mahwah, NJ: Lawrence Erlbaum Associates. Saad, S., & Sackett, P. R. (2002). Investigating differential prediction by gender in employment-oriented personality measures. Journal of Applied Psychology, 87, 667–674. Sackett, P. R., & Wilk, S. L. (1994). Within-group norming and other forms of adjustment in preemployment testing. American Psychologist, 49, 929–954. Saucier, G., & Goldberg, L. R. (2003). The structure of personality attributes. In M. R. Barrick & A. M. Ryan (Eds.), Personality and work (pp. 1–29). San Francisco: Jossey-Bass. Schmitt, N., Rogers, W., Chan, D., Sheppard, L., & Jennings, D. (1997). Adverse impact and predictive efficiency of various predictor combinations. Journal of Applied Psychology, 82, 719–730. Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality measures as predictors of job performance. A meta-analytic review. Personnel Psychology, 44, 703–742. Uniform Guidelines on Employee Selection (1978). Federal Register, 43, 38290–38315. Wiggins, J. S. (1991). Agency and communion as conceptual coordinates for the understanding and measurement of interpersonal behaviour. In W. M. Grove & D. Cicchetti (Eds.). Thinking clearly about psychology (Vol. 2). Minneapolis: University of Minnesota Press. Wiggins, J. S., & Trapnell, P. D. (1996). A dyadic-interactional perspective on the fivefactor model. In J. S. Wiggins (Ed.), The five-factor model of personality: Theoretical perspectives (pp. 88–162). New York: Guilford. Women and Work Commission (2006). Shaping a fairer future. London: Women and Work Commission.