Regularized Latent Class Analysis with Application in Cognitive Diagnosis

作     者：Chen, Yunxiao Li, Xiaoou Liu, Jingchen Ying, Zhiliang 

作者机构：Emory Univ Atlanta GA 30322 USA Univ Minnesota Minneapolis MN 55455 USA Columbia Univ New York NY 10027 USA 

出 版 物：《PSYCHOMETRIKA》 (心理测量学)

年 卷 期：2017年第82卷第3期

页      面：660-692页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 04[教育学] 0701[理学-数学] 

基　　金：NSF [SES-1323977, IIS-1633360] Army Research Office [W911NF-15-1-0159] NIH [R01GM047845] 

主　　题：diagnostic classification models latent class analysis regularization consistency EM algorithm 

摘      要：Diagnostic classification models are confirmatory in the sense that the relationship between the latent attributes and responses to items is specified or parameterized. Such models are readily interpretable with each component of the model usually having a practical meaning. However, parameterized diagnostic classification models are sometimes too simple to capture all the data patterns, resulting in significant model lack of fit. In this paper, we attempt to obtain a compromise between interpretability and goodness of fit by regularizing a latent class model. Our approach starts with minimal assumptions on the data structure, followed by suitable regularization to reduce complexity, so that readily interpretable, yet flexible model is obtained. An expectation-maximization-type algorithm is developed for efficient computation. It is shown that the proposed approach enjoys good theoretical properties. Results from simulation studies and a real application are presented.