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Discovering Structural Equation Modeling Using Stata, by Alan Acock,
successfully introduces both the statistical principles involved in structural
equation modeling (SEM) and the use of Stata to fit these models. The book
uses an application-based approach to teaching SEM. Acock demonstrates how to
fit a wide variety of models that fall within the SEM framework and provides
datasets that enable the reader to follow along with each example. As each
type of model is discussed, concepts such as identification, handling of
missing data, model evaluation, and interpretation are covered in detail.
In Stata, structural equation models can be fit using the command language or
the graphical user interface (GUI) for SEM, known as the SEM Builder. The
book demonstrates both of these approaches. Throughout the text, the
examples use the sem command. Each chapter also includes brief
discussions on drawing the appropriate path diagram and performing estimation
from within the SEM Builder. A more in-depth coverage of the SEM Builder is
given in one of the book’s appendixes.
The first two chapters introduce the building blocks of SEM. Chapter 1
begins with overviews of Cronbach’s alpha as a measure of reliability
and of exploratory factor analysis. Then, building on these concepts, Acock
demonstrates how to perform confirmatory factor analysis, discusses a variety
of statistics available for assessing the fit of the model, and shows a more
general measurement of reliability that is based on confirmatory factor
analysis. Chapter 2 focuses on using SEM to perform path analysis. It
includes examples of mediation, moderation, cross-lagged panel models, and
nonrecursive models.
Chapter 3 demonstrates how to combine the topics covered in the first two
chapters to fit full structural equation models. The use of modification
indices to guide model modification and computation of direct, indirect, and
total effects for full structural equation models are also covered.
Chapter 4 details the application of SEM to growth curve modeling. After
introducing the basic linear latent growth curve model, Acock extends this to
more complex cases such as the inclusion of quadratic terms, time-varying
covariates, and time-invariant covariates.
In chapter 5, Acock discusses testing for differences across groups in SEM.
He introduces the specialized sem syntax for multiple-group models and
discusses the intricacies of testing for group differences for the different
types of models presented in the preceding chapters.
Discovering Structural Equation Modeling Using Stata is an excellent
resource both for those who are new to SEM and for those who are familiar
with SEM but new to fitting these models in Stata. It is useful as a text
for courses covering SEM as well as for researchers performing SEM.
For further details or to order online, please visit the
Stata Bookstore.
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