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

Ick Hoon Jin, Minjeong Jeon

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)236-260
Number of pages25
Issue number1
Publication statusPublished - 2019 Mar 15


All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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