Journal Article

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

Jeon, Minjeong, Jin, Ick Hoon

2019

Overview

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.

• Publisher: Springer US,Springer Nature B.V
• Subject: Adolescent / Assessment / Bayes Theorem / Behavioral Science and Psychology / Child / Cognition / Cognitive Ability / Computer Simulation / Data Analysis / Data Interpretation, Statistical / Data processing / Feedback (Response) / Female / Humanities / Humans / Intellectual Disciplines / Item response theory / Law / Male / Markov Chains / Models, Statistical / Monte Carlo Method / Multidimensional Scaling / Networks / Probability / Psychology / Psychology, Child / Psychometrics / Psychometrics - methods / Quantitative psychology / Statistical Data / Statistical Theory and Methods / Statistics for Social Sciences / Test Items / Testing and Evaluation / Thinking