Investigating the Convergent, Discriminant, and Predictive Validity of the Mental Toughness Situational Judgment Test

This study investigated the validity of scores of a workplace-based measure of mental toughness, the Mental Toughness Situational Judgment Test (MTSJT). The goal of the study was to determine if MTSJT scores predicted supervisor ratings 1) differentially compared to other measures of mental toughness, grit, and resilience, and 2) incrementally beyond cognitive ability and conscientiousness. Further, two machine learning algorithms – elastic nets and random forests – were used to model predictions at both the item and scale level. MTJST scores provided the most accurate predictions overall when model at the item level via a random forest approach. The MTSJT was the only measure to consistently provide incremental validity when predicting supervisor ratings. The results further emphasize the growing importance of both mental toughness and machine learning algorithms to industrial/organizational psychologists.