期刊
PSYCHOMETRIKA
卷 87, 期 1, 页码 83-106出版社
SPRINGER
DOI: 10.1007/s11336-021-09781-2
关键词
Gaussian mixture model; Gaussian graphical model; ordinal data; latent variables; network psychometrics; EM algorithm
类别
资金
- National Science Foundation (NSF) [DMS-1811552, DMS-1953189, CCF-2007823]
Researchers introduced finite mixture of ordinal graphical models to study the heterogeneous conditional dependence relationships of ordinal data in psychological science. By developing a penalized likelihood approach and designing a generalized EM algorithm, the significant computational challenges were effectively addressed, demonstrating good performance in simulation studies and real applications.
Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. However, ordinal variables are very popular in many areas of psychological science, and the population often consists of several different groups based on the heterogeneity in ordinal data. Driven by these needs, we introduce the finite mixture of ordinal graphical models to effectively study the heterogeneous conditional dependence relationships of ordinal data. We develop a penalized likelihood approach for model estimation, and design a generalized expectation-maximization (EM) algorithm to solve the significant computational challenges. We examine the performance of the proposed method and algorithm in simulation studies. Moreover, we demonstrate the potential usefulness of the proposed method in psychological science through a real application concerning the interests and attitudes related to fan avidity for students in a large public university in the United States.
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