Effects of Applicant Faking on Forced-Choice and Likert Scores

We introduce a novel, regression-based moderation framework to model faking effects that incorporates evaluation of faking tendency as a moderator. We also consider how perceived trait desirability may be factored into the framework and provide programming code for applied researchers to utilize the method in their research. Using this framework, we revisit a well-known response format (i.e., forced-choice) to formally evaluate its ability to mitigate the effects of applicant faking as compared to the widely used Likert format. The impetus for the latter evaluation stems from the use of item response theory (IRT) modeling to yield non-ipsative scores from forced-choice measures. We found strong support for the need to incorporate moderating effects of faking tendency and desirability in predicting applicants’ responses. Also, we found that the only substantial difference across formats lies in forced-choice scores yielding a lower mean inflation at high faking values. As a result, forced-choice scores do not outperform Likert scores when selection ratios are used but may be beneficial when cutoff scores are used. Application of the moderation framework presented extends to self-reported construct measures of varied kinds.