FAKING IN PERSONNEL SELECTION: TRADEOFFS IN PERFORMANCE VERSUS FAIRNESS RESULTING FROM TWO CUT‐SCORE STRATEGIES

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. This study deals with faking on self-report personality instruments and proposes a cut-score method that minimizes the displacement of deserving applicants by fakers (by “deserving applicants,” we simply mean those who would have been selected if no faking had occurred; we will use the term “deserving applicants” throughout the rest of this paper). Faking, in this context, refers to a focused, intentional effort to respond in a socially desirable manner on a personality test in order to increase the likelihood of attaining a job. That the faking behavior is intentional and focused on the specific goal of increasing test performance to attain employment is