Bayesian structural equation modeling and its current researches

doi:10.3724/SP.J.1042.2019.01812

Abstract

Abstract:

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.

Key words: structural equation modeling, Bayesian estimation, maximum-likelihood estimation

CLC Number:

B841

ZHANG Lijin, LU Jiaqi, WEI Xiayan, PAN Junhao. Bayesian structural equation modeling and its current researches[J]. Advances in Psychological Science, 2019, 27(11): 1812-1825.

Figures/Tables 3

References 80

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