Item response theory—a statistical framework for educational and psychological measurement

Yunxiao Chen, Xiaoou Li, Jingchen Liu and Zhiliang Ying

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Item response theory (IRT) has become one of the most popular statistical models for psychometrics, a field of study concerned with the theory and techniques of psychological measurement. The IRT models are latent factor models tailored to the analysis, interpretation and prediction of individuals’ behaviors in answering a set of measurement items that typically involve categorical response data. Many important questions of measurement are directly or indirectly answered through the use of IRT models, including scoring individuals’ test performances, validating a test scale, linking two tests, among others. This paper provides a review of item response theory, including its statistical framework and psychometric applications. We establish connections between item response theory and related topics in statistics, including empirical Bayes, nonparametric methods, matrix completion, regularized estimation and sequential analysis. Possible future directions of IRT are discussed from the perspective of statistical learning.

Keywords: psychometrics; measurement theory; factor analysis; item response theory; latent trait; validity; reliability (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2025-06-02
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Published in Statistical Science, 2, June, 2025, 40(2), pp. 167 - 194. ISSN: 0883-4237

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