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Variational Estimation for Multidimensional Generalized Partial Credit Model

Published online by Cambridge University Press:  01 January 2025

Chengyu Cui ,
Chun Wang  and
Gongjun Xu Open the ORCID record for Gongjun Xu [Opens in a new window]
Chengyu Cui
Affiliation:
University of Michigan
Chun Wang*
Affiliation:
University of Washington
Gongjun Xu*
Affiliation:
University of Michigan
*
Correspondence should be made to ChunWang, College of Education, University ofWashington, 312 EMiller Hall, 2012 Skagit Lane, Seattle, WA98105, USA. Email: wang4066@uw.edu
Correspondence should be made to Gongjun Xu, Department of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, MI48109, USA. Email: gongjun@umich.edu
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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.

Keywords

marginal maximum likelihood estimationvariational methodmultidimensional item response theoryexpectation-maximization algorithm

Information

Type
Theory & Methods
Information
Psychometrika , Volume 89 , Issue 3 , September 2024 , pp. 929 - 957
Copyright
Copyright © 2024 The Author(s), under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-024-09955-8.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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