There are methods in personnel selection that seeks to improve diversity in the hiring outcomes. Among them, one of the most promising methods is Pareto-optimal weighting via the normal boundary intersection algorithm (De Corte et al.
In this project, we introduce another Pareto-optimal weighting method that incorporates a priori inputs regarding relative importance of criterion validity (i.e., expected job performance) and diversity in hiring outcomes.
This project is in collaboration with Daniel A.
Scholars have argued that faking happens on the item-level (KKuncel & Tellegen, 2001) and has complex patterns (e.g., non-linearity; Kuncel & Borneman, 2007).
In this project, we put this argument directly to test.