Substantial research has been dedicated to examining and combating respondent misrepresentation (i.e., “faking”) on personality assessments. Two approaches to combat faking that have garnered particular attention include: (a) designing systems to identify likely fakers and (b) developing difficult-to-fake measures. Consistent with suggestions to combine these strategies, the present article examines a new faking detection system specifically designed for a difficult-to-fake measure (i.e., the Conditional Reasoning Test for Aggression; CRT-A). Four studies (a) help elucidate the conditions under which the CRT-A is fakeable, (b) provide initial construct validity evidence for the faking detection system developed here, (c) examine the effects of faking and faking detection on the CRT-A’s criterion-oriented validity, and (d) show that participants identify CRT-based faking detection items at worse-than-chance levels even when they are fully informed about how these items work. Taken together, these studies reinforce the importance of maintaining the indirect nature of CRTs but also show that the faking detection system developed here represents a promising method of identifying those who may have used inside information to manipulate their scores.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Strategy and Management
- Management of Technology and Innovation