贝叶斯结构方程模型及其研究现状

Bayesian structural equation modeling and its current researches

在心理学研究中结构方程模型(Structural Equation Modeling,SEM)被广泛用于检验潜变量间的因果效应,其估计方法有频率学方法(如,极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势,贝叶斯结构方程模型发展迅速,但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性,及几类常用的贝叶斯结构方程模型及其应用现状,旨在为应用研究者介绍新的研究工具。

Structural equation modeling (SEM) has been widely used in psychological researches to investigate the casual relationship among latent variables. Model estimation can be conducted under both the frequentist framework (e.g., maximum-likelihood approach) and the Bayesian framework. In recent years, with the prevalence of Bayesian statistics and its advantages in dealing with small samples, missing data and complex models in SEM, Bayesian structural equation modeling (BSEM) has developed rapidly. However, in China its application in the field of psychology is still insufficient. Therefore, this paper mainly focuses on presenting this new research method to applied researchers. We explain the theoretical and methodological basis of BSEM, as well as its advantages and disadvantages compared with the traditional frequentist approach. We also introduce several commonly used BSEM models and their applications.