Variational Estimation for Multidimensional Generalized Partial Credit Model.

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Psychometrika Pub Date : 2024-09-01 Epub Date: 2024-03-01 DOI:10.1007/s11336-024-09955-8
Chengyu Cui, Chun Wang, Gongjun Xu
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引用次数: 0

Abstract

Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap, this paper presents a novel Gaussian variational estimation algorithm for the multidimensional generalized partial credit model. The proposed algorithm demonstrates both fast and accurate performance, as illustrated through a series of simulation studies and two real data analyses.

Abstract Image

多维广义部分信用模型的变量估计。
多维项目反应理论(MIRT)模型在心理测量学文献中引起了越来越多的关注。人们已经开发出了估算二分式反应的 MIRT 模型的高效方法,但为多分式模型构建同样高效、稳健的算法却受到了有限的关注。为了弥补这一不足,本文提出了一种新的多维广义部分信用模型高斯变分估计算法。通过一系列模拟研究和两个真实数据分析,本文提出的算法展示了快速和准确的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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