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

Ick Hoon Jin () and Minjeong Jeon
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Ick Hoon Jin: University of Notre Dame
Minjeong Jeon: University of California, Los Angeles

Psychometrika, 2019, vol. 84, issue 1, No 12, 236-260

Abstract: 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 model; multilayer network; item response model; local dependence; cognitive assessment (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11336-018-9630-0

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