Book description
This book provides clear instructions to researchers on how to apply
Structural Equation Models (SEMs) for analyzing the inter
relationships between observed and latent variables.
Basic and Advanced Bayesian Structural Equation Model
ing
introduces basic and advanced SEMs for analyzing various kinds of
complex data, such as ordered and unordered categorical data, multilevel
data, mixture data, longitudinal data, highly non-normal data, as well
as some of their combinations. In addition, Bayesian semiparametric SEMs
to capture the true distribution of explanatory latent variables are
introduced, whilst SEM with a nonparametric structural equation to
assess unspecified functional relationships among latent variables are
also explored.
Statistical methodologies are developed using the Bayesian approach
giving reliable results for small samples and allowing the use of prior
information leading to better statistical results. Estimates of the
parameters and model comparison statistics are obtained via powerful
Markov Chain Monte Carlo methods in statistical computing.
Introduces the Bayesian approach to SEMs, including discussion on the
selection of prior distributions, and data augmentation. Demonstrates
how to utilize the recent powerful tools in statistical computing
including, but not limited to, the Gibbs sampler, the Metropolis-Hasting
algorithm, and path sampling for producing various statistical results
such as Bayesian estimates and Bayesian model comparison statistics in
the analysis of basic and advanced SEMs. Discusses the Bayes factor,
Deviance Information Criterion (DIC), and $L_nu$-measure for Bayesian
model comparison. Introduces a number of important generalizations of
SEMs, including multilevel and mixture SEMs, latent curve models and
longitudinal SEMs, semiparametric SEMs and those with various types of
discrete data, and nonparametric structural equations. Illustrates how
to use the freely available software WinBUGS to produce the results.
Provides numerous real examples for illustrating the theoretical
concepts and computational procedures that are presented throughout the
book. Researchers and advanced level students in statistics,
biostatistics, public health, business, education, psychology and social
science will benefit from this book.
