摘要：Motivated by a large multilevel survey conducted by the US Veterans
Health Administration (VHA), we propose a structural equations model which in-
volves a set of latent variables to capture dependence between di
erent responses, a
set of facility level random e
ects to capture facility heterogeneity and dependence
between individuals within the same facility, and a set of covariates to account for
individual heterogeneity. Identi ability associated with structural equations mod-
eling is addressed and properties of the proposed model are carefully examined. An
e
ective and practically useful modeling strategy is developed to deal with missing
responses and to model missing covariates in the structural equations framework.
Markov chain Monte Carlo sampling is used to carry out Bayesian posterior com-
putation. Several variations of the proposed model are considered and compared
via the deviance information criterion. A detailed analysis of the VHA all employee
survey data is presented to illustrate the proposed methodology.
关键词：DIC, Latent variable, Markov chain Monte Carlo, missing at random,
random e
ects, VHA all employee survey data
