Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 21, Issue 2, Pages 253-262Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10705511.2014.882688
Keywords
latent variables; Bayesian inference; Markov chain Monte Carlo; manifest variables; mediation effect
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Recently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., the z test using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice.
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