Premium
Bayesian generalized structured component analysis
Author(s) -
Choi Ji Yeh,
Hwang Heungsun
Publication year - 2020
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12166
Subject(s) - structural equation modeling , markov chain monte carlo , bayesian probability , gibbs sampling , latent variable , computer science , econometrics , moderation , component (thermodynamics) , multilevel model , range (aeronautics) , markov chain , data mining , mathematics , statistics , machine learning , artificial intelligence , physics , materials science , composite material , thermodynamics
Generalized structured component analysis ( GSCA ) is a component‐based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster‐level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA , called BGSCA , is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA . We conduct a simulation study to evaluate the performance of BGSCA . We also apply BGSCA to real data to demonstrate its empirical usefulness.