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.
General
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