Item Response Theory -- A Statistical Framework for Educational and Psychological Measurement
Item Response Theory -- A Statistical Framework for Educational and Psychological Measurement
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发表时间：
2021-08
期刊：
影响因子：
3
通讯作者：
Yunxiao Chen;Xiaoou Li;Jingchen Liu;Z. Ying
中科院分区：
文献类型：
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作者：
Yunxiao Chen;Xiaoou Li;Jingchen Liu;Z. Ying
项目反应理论（IRT）是心理测量学中最流行的统计模型之一，是心理测量理论和技术的研究领域。IRT模型是一种潜在因素模型，用于分析、解释和预测个体在回答一组通常涉及分类回答数据的测量项目时的行为。许多重要的测量问题直接或间接地通过使用IRT模型来回答，包括对个体的测试表现进行评分，验证测试量表，连接两个测试等等。本文对项目反应理论的统计框架和心理测量学应用进行了综述。我们建立项目反应理论和统计学相关主题之间的联系，包括经验贝叶斯，非参数方法，矩阵完成，正则化估计和序列分析。从统计学习的角度讨论了项目反应理论未来可能的发展方向。
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.