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.
Statistical Analysis of Large-scale Item Response Data under Measurement Non-invariance: A Statistically Consistent Method and Its Application to PISA 2022
An efficient adaptive dimension selection algorithm for multidimensional probit graded response models
KLATASDS-MOE；School of Statistics, East China Normal University（东华大学统计学院）；Zhiyuan School of Liberal Arts,Beijing Institute of Petrochemical Technology（北京石油化学工业技术学院人文学院）
