Using a supervised machine learning algorithm for detecting faking good in a personality self-report
We developed a supervised machine learning classifier to identify faking good by analyzing item response patterns of a Big Five personality self‐report. We used a between‐subject design, dividing participants (N = 548) into two groups and manipulated their faking behavior via instructions given prior to administering the self‐report. We implemented a simple classifier based on the Lie scale's cutoff score and several machine learning models fitted either to the personality scale scores or to the items response patterns.