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A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data

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  • Ick Hoon Jin

    (University of Notre Dame)

  • Minjeong Jeon

    (University of California, Los Angeles)

Abstract

Item response theory (IRT) is one of the most widely utilized tools for item response analysis; however, local item and person independence, which is a critical assumption for IRT, is often violated in real testing situations. In this article, we propose a new type of analytical approach for item response data that does not require standard local independence assumptions. By adapting a latent space joint modeling approach, our proposed model can estimate pairwise distances to represent the item and person dependence structures, from which item and person clusters in latent spaces can be identified. We provide an empirical data analysis to illustrate an application of the proposed method. A simulation study is provided to evaluate the performance of the proposed method in comparison with existing methods.

Suggested Citation

  • Ick Hoon Jin & Minjeong Jeon, 2019. "A Doubly Latent Space Joint Model for Local Item and Person Dependence in the Analysis of Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 236-260, March.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:1:d:10.1007_s11336-018-9630-0
    DOI: 10.1007/s11336-018-9630-0
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    References listed on IDEAS

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    Cited by:

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    2. Park, Jaewoo & Jin, Ick Hoon & Schweinberger, Michael, 2022. "Bayesian model selection for high-dimensional Ising models, with applications to educational data," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
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    4. Ick Hoon Jin & Minjeong Jeon & Michael Schweinberger & Jonghyun Yun & Lizhen Lin, 2022. "Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1225-1244, November.

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