Improving Latent Trait Estimation in Multidimensional Forced Choice Measures: Latent Regression Multi-Unidimensional Pairwise Preference Model

The field of psychometrics has made remarkable progress in developing item response theory (IRT) models for analyzing multidimensional forced choice (MFC) measures. This study introduces an innovative method that enhances the latent trait estimation of the Multi-Unidimensional Pairwise Preference (MUPP) model by incorporating latent regression modeling. To validate the efficacy of the new method, we conducted a comprehensive simulation study. The results of the study provide compelling evidence that the proposed latent regression MUPP (LR-MUPP) model significantly improves the accuracy of the latent trait estimation. This study opens new avenues for future research and encourages further development and refinement of MFC IRT models and their applications.