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Variational Estimation for Multidimensional Generalized Partial Credit Model

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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.

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Acknowledgements

This work is partially supported by IES grant R305D200015 and NSF grants SES-1846747 and SES-2150601.

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Authors and Affiliations

  1. Department of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, MI, 48109, USA

    Chengyu Cui & Gongjun Xu

  2. College of Education, University of Washington, 312 E Miller Hall, 2012 Skagit Lane, Seattle, WA, 98105, USA

    Chun Wang

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  1. Chengyu Cui
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  2. Chun Wang
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  3. Gongjun Xu
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Correspondence to Chun Wang or Gongjun Xu.

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Cui, C., Wang, C. & Xu, G. Variational Estimation for Multidimensional Generalized Partial Credit Model. Psychometrika (2024). https://doi.org/10.1007/s11336-024-09955-8

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