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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.