Comparing Approaches to Estimating Person Parameters for the MUPP Model

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.