Investigating the Convergent, Discriminant, and Predictive Validity of the Mental Toughness Situational Judgment Test
Flannery, Nicholas Martin
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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.
General Audience Abstract
The study investigated whether the Mental Toughness Situational Judgment Test (MTSJT)– a measure of mental toughness directly in the workplace, could predict employees' supervisor ratings. Further, the study aimed to understand if the MTSJT was a better predictor than other measures of mental toughness, grit, resilience, intelligence, and conscientiousness. The study used machine learning algorithms to generate predictive models using both question-level scores and scale-level scores. The results suggested that the MTSJT scores predicted supervisor ratings at both the question and scale level using a random forest model. Further, the MTJST was a better predictor than most other measures included in the study. The results emphasize the growing importance of both mental toughness and machine learning algorithms to industrial/organizational psychologists.
- Doctoral Dissertations