Predicting Naval Academy Performance Using Holistic Personality Analysis in Multidimensional Space
Abstract
Psychological models of personality have used trait measures to index individuals with respect to specific traits or categorical types to binindividuals into defined personality types. Both of these approaches may be suboptimal for predicting performance. Categorical models rely onrough dichotomization of data and trait models may overfit variance to rigid traits and obscure trait interaction effects. This project comparedpredictions of midshipmen performance and outcomes at the United States Naval Academy, specifically comparing predictions derived fromregression techniques with standard traits and type variables with predictions using machine learning techniques, such as k-nearest neighbors andboosted random forests. Using data from recent Naval Academy graduates (N = 725), we first applied traditional penalized regression techniques topredict performance, specifically academic and military order of merit at graduation, using standard personality traits, types, and values. Thesepredictions served as the baseline for assessing the quality of prediction from the selected machine learning techniques. We next built, optimized,and analyzed machine learning models, finding that their accuracies were, at best, at the level of traditional penalized regression models. Finally,we examined the optimization process of the machine learning models to identify potential optimum dimensionalities for personality predictions,finding it matches currently accepted models of personality. While the machine learning models were more complex, computationally expensive,and less interpretable, we found they did not outperform regression models.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 12, 2021
- Accession Number
- AD1149682
Entities
People
- Philip B. Smith
Organizations
- United States Naval Academy