Comparing Approaches to Estimating Person Parameters for the MUPP Model.

IF 1 4区心理学 Q4 PSYCHOLOGY, MATHEMATICAL

Applied Psychological Measurement Pub Date : 2025-01-27 DOI:10.1177/01466216251316278

David M LaHuis, Caitlin E Blackmore, Gage M Ammons

引用次数: 0

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.

查看原文本刊更多论文

MUPP模型中人参数估计方法的比较。

本研究比较了最大后验（MAP）、期望后验（EAP）和马尔可夫链蒙特卡罗（MCMC）方法在多维成对偏好模型中计算人得分的方法。MCMC方法使用无掉头抽样（NUTS）。结果表明，在低维数情况下，完全交叉正交的EAP和NUTS表现较好。此外，NUTS在较大尺寸条件下产生了最准确的估计。每个维度的项目数对人参数恢复的影响最大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Applied Psychological Measurement Multiple-

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.