A Forced-Choice Neural Cognitive Diagnostic Model of Personality Testing

TL;DR

This paper introduces a deep learning-based model for personality testing that improves accuracy and interpretability in forced-choice assessments, addressing limitations of traditional methods through neural networks and monotonicity assumptions.

Contribution

The study develops a novel neural diagnostic model specifically designed for forced-choice personality tests, enhancing interpretability and robustness over existing models.

Findings

High accuracy on real-world datasets

Enhanced interpretability of participant and item parameters

Robustness against various data conditions

Abstract

In the smart era, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. This study presents a deep learning-based Forced-Choice Neural Cognitive Diagnostic Model (FCNCD) that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items in forced-choice tests, we create interpretable participant and item parameters. We model the interactions between participant and item features using multilayer neural networks after mining them using nonlinear mapping. In addition, we use the monotonicity assumption to improve the interpretability of the diagnostic results. The FCNCD's effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness.