Structural Equation Modeling (eBook)

***Winner of the 2008 Ziegel Prize for outstanding new book of
the year*** Structural equation modeling (SEM) is a powerful multivariate
method allowing the evaluation of a series of simultaneous
hypotheses about the impacts of latent and manifest variables on
other variables, taking measurement errors into account. As SEMs
have grown in popularity in recent years, new models and
statistical methods have been developed for more accurate analysis
of more complex data. A Bayesian approach to SEMs allows the use of
prior information resulting in improved parameter estimates, latent
variable estimates, and statistics for model comparison, as well as
offering more reliable results for smaller samples.

Structural Equation Modeling introduces the Bayesian
approach to SEMs, including the selection of prior distributions
and data augmentation, and offers an overview of the
subject's recent advances.

* Demonstrates how to utilize powerful statistical computing
tools, including the Gibbs sampler, the Metropolis-Hasting
algorithm, bridge sampling and path sampling to obtain the Bayesian
results.

* Discusses the Bayes factor and Deviance Information Criterion
(DIC) for model comparison.

* Includes coverage of complex models, including SEMs with
ordered categorical variables, and dichotomous variables, nonlinear
SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with
missing data, SEMs with variables from an exponential family of
distributions, and some of their combinations.

* Illustrates the methodology through simulation studies and
examples with real data from business management, education,
psychology, public health and sociology.

* Demonstrates the application of the freely available software
WinBUGS via a supplementary website featuring computer code and
data sets.

Structural Equation Modeling: A Bayesian Approach is a
multi-disciplinary text ideal for researchers and students in many
areas, including: statistics, biostatistics, business, education,
medicine, psychology, public health and social science.