Lab-field comparisons of self-presentation on personality measures: A meta-analysis

Hooper, Amy C.; Sackett, Paul R.

doi:10.1037/e518532013-574

Cited by 13 publications

(23 citation statements)

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“…Interestingly, consistent with past research (Hooper & Sackett, ), the standard deviation of scales at both domain and facet‐level was around 20% lower in the applicant sample. We propose several explanations for this.…”

Section: Discussionsupporting

confidence: 90%

Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

et al. 2017

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The present study evaluated the ability of item‐level bifactor models (a) to provide an alternative explanation to current theories of higher order factors of personality and (b) to explain socially desirable responding in both job applicant and non‐applicant contexts. Participants (46% male; mean age = 42 years, SD = 11) completed the 200‐item HEXACO Personality Inventory‐Revised either as part of a job application (n = 1613) or as part of low‐stakes research (n = 1613). A comprehensive set of invariance tests were performed. Applicants scored higher than non‐applicants on honesty‐humility (d = 0.86), extraversion (d = 0.73), agreeableness (d = 1.06), and conscientiousness (d = 0.77). The bifactor model provided improved model fit relative to a standard correlated factor model, and loadings on the evaluative factor of the bifactor model were highly correlated with other indicators of item social desirability. The bifactor model explained approximately two‐thirds of the differences between applicants and non‐applicants. Results suggest that rather than being a higher order construct, the general factor of personality may be caused by an item‐level evaluative process. Results highlight the importance of modelling data at the item‐level. Implications for conceptualizing social desirability, higher order structures in personality, test development, and job applicant faking are discussed. Copyright © 2017 European Association of Personality Psychology

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Section: Discussionsupporting

confidence: 90%

Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

et al. 2017

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“…Judge & Zapata, ). However, it should be kept in mind that previous research has suggested that lab and field approaches converge substantially: this has been found in faking research (Hooper & Sackett, ) and in research on the relation between lab and field research (Mitchell, ; Vanhove & Harms, ). Second, we used scenarios in which supervisors had no motivation to fake bad (cf.…”

Section: Discussionmentioning

confidence: 99%

“…We were able to link the questionnaires because of a code that each participant generated for him‐/herself. The order (the application condition before the honest condition) was based on the meta‐analytical results (Hooper & Sackett, ) that lab studies with such an order produce results that are more consistent with field research than the alternative order.…”

Section: Methodsmentioning

confidence: 99%

Are observer ratings of applicants’ personality also faked? Yes, but less than self‐reports

König

Thommen

Wittwer

et al. 2017

Int J Selection Assessment

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Although self‐report personality tests are a comparatively cheap and easy‐to‐administer personnel selection tool, researchers have criticized them for not predicting enough criterion‐related variance. Researchers have suggested using observer‐ratings of personality (e.g., as part of a reference check from a supervisor) because observer‐ratings have been reported to be more predictive. However, it is theoretically and empirically unclear whether supervisors also engage in faking (the intentional distortion of responses). Study 1 explored faking among managers who were first asked to imagine that a subordinate had to leave his/her job for private reasons and then to rate the personality of the subordinate. A week later, managers rated their subordinates honestly. A repeated‐measures MANOVA indicated that managers did fake. Study 2 (among supervisors of working students) replicated the above findings but also showed that there is less faking in supervisor‐ratings than in self‐ratings. Furthermore, we found no evidence that the validity of personality scales for predicting academic performance depends on self‐ versus observer‐ratings or on an applicant versus an honest condition. These two studies thus show that practitioners should not equate personality ratings obtained from observers in a selection context with honest personality ratings.

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“…Conventional wisdom has been that different mean faking estimates are arrived at depending on whether one derives estimates from lab versus field settings. However, a recent meta‐analysis (Hooper & Sackett, 2008) demonstrated that, once one accounts for various methodological factors in lab (e.g., between‐ vs. within‐subjects designs, order effects, whether respondents were simply told to “fake good” vs. “respond as an applicant”) and field (e.g., whether applicants and incumbents were drawn from the same population) studies, both lab and field studies converge on similar estimates of score increases due to faking for each of the Big Five personality dimensions. Hooper and Sackett report d s ranging from about .40 to 1.0, with the largest increases for Conscientiousness and Emotional Stability.…”

Section: Methodsmentioning

confidence: 99%

“…That is, engagement in impression management on a daily basis is common, so even relatively unmotivated samples, such as job incumbents, are likely distorting their self‐report personality responses to some degree. However, when asked to “fake good,” respondents can increase their personality test scores (Berry, Sackett, & Wiemann, 2007; Ones & Viswesvaran, 1998), and job applicants generally score significantly higher on personality tests than do job incumbents (Hooper & Sackett, 2008), demonstrating that, when motivated to do so, persons can engage in a focused, intentional effort to increase their personality scores beyond their nonapplicant scores that already reflect daily impression management. This focused, intentional effort to increase one's score in order to attain a job is what is meant by “faking” in this study.…”

mentioning

confidence: 99%

Faking in Personnel Selection: Tradeoffs in Performance Versus Fairness Resulting From Two Cut‐score Strategies

Berry

Sackett

2009

Personnel Psychology

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Most faking research has examined the use of personality measures when using top‐down selection. We used simulation to examine the use of personality measures in selection systems using cut scores and outlined a number of issues unique to these situations. In particular, we compared the use of 2 methods of setting cut scores on personality measures: applicant‐data‐derived (ADD) and nonapplicant‐data‐derived (NADD) cut‐score strategies. We demonstrated that the ADD strategy maximized mean performance resulting from the selection system in the face of applicant faking but that this strategy also resulted in the displacement of deserving applicants by fakers (which has fairness implications). On the other hand, the NADD strategy minimized displacement of deserving applicants but at the cost of some mean performance. Therefore, the use of the ADD versus NADD strategies can be viewed as a strategic decision to be made by the organization, as there is a tradeoff between the 2 strategies in effects on performance versus fairness to applicants. We quantitatively outlined these tradeoffs at various selection ratios, levels of validity, and amounts of faking in the applicant pool.

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Lab-field comparisons of self-presentation on personality measures: A meta-analysis

Cited by 13 publications

References 0 publications

Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

Are observer ratings of applicants’ personality also faked? Yes, but less than self‐reports

Faking in Personnel Selection: Tradeoffs in Performance Versus Fairness Resulting From Two Cut‐score Strategies

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