Bayesian analysis of two-level nonlinear structural equation models with continuous and polytomous data.
Two-level structural equation models with mixed continuous and polytomous data and nonlinear structural equations at both the between-groups and within-groups levels are important but difficult to deal with. A Bayesian approach is developed for analysing this kind of model. A Markov chain Monte Carlo procedure based on the Gibbs sampler and the Metropolis-Hasting algorithm is proposed for producing joint Bayesian estimates of the thresholds, structural parameters and latent variables at both levels. Standard errors and highest posterior density intervals are also computed. A procedure for computing Bayes factor, based on the key idea of path sampling, is established for model comparison.
Song
Xin-Yuan
XY
Department of Statistics, The Chinese University of Hong Kong.
Lee
Sik-Yum
SY
D001499:Bayes Theorem; D006801:Humans; D008960:Models, Psychological; D017711:Nonlinear Dynamics