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

Published online by Cambridge University Press:  01 January 2025

Ick Hoon Jin  and
Minjeong Jeon
Ick Hoon Jin*
Affiliation:
University of Notre Dame
Minjeong Jeon
Affiliation:
University of California, Los Angeles
*
Correspondence should be made to Ick Hoon Jin, Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA . Email: [email protected]
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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.

Keywords

latent space modelmultilayer networkitem response modellocal dependencecognitive assessment
Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 1 , 15 March 2019 , pp. 236 - 260
Copyright
Copyright © 2018 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-9630-0) contains supplementary material, which is available to authorized users.

Both Ick Hoon Jin and Minjeong Jeon are first authors.

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