Comparing Approaches to Estimating Person Parameters for the MUPP Model

Authors

David M. LaHuis, Wright State University - Main CampusFollow
Caitlin E. Blackmore, Aon Hewitt
Gage M. Ammons, Wright State University

Document Type

Article

Publication Date

1-1-2025

Identifier/URL

42298627 (Pure); 39885981 (PubMed); PMC11775930 (PubMedCentral)

Abstract

This study compared maximum a posteriori (MAP), expected a posteriori (EAP), and Markov Chain Monte Carlo (MCMC) approaches to computing person scores from the Multi-Unidimensional Pairwise Preference Model. The MCMC approach used the No-U-Turn sampling (NUTS). Results suggested the EAP with fully crossed quadrature and the NUTS outperformed the others when there were fewer dimensions. In addition, the NUTS produced the most accurate estimates in larger dimension conditions. The number of items per dimension had the largest effect on person parameter recovery.

Comments

Publisher Copyright: © The Author(s) 2025.

Repository Citation

LaHuis, D. M., Blackmore, C. E., & Ammons, G. M. (2025). Comparing Approaches to Estimating Person Parameters for the MUPP Model. Applied Psychological Measurement.
https://corescholar.libraries.wright.edu/psychology/638

DOI

10.1177/01466216251316278