This book presents a basic introduction to structural equation modeling
(SEM). Readers will find that we have kept to our tradition of keeping
examples rudimentary and easy to follow. The reader is provided with
a review of correlation and covariance, followed by multiple regression,
path, and factor analyses in order to better understand the building blocks
of SEM. The book describes a basic structural equation model followed by
the presentation of several different types of structural equation models.
Our approach in the text is both conceptual and application oriented.
Each chapter covers basic concepts, principles, and practice and then
utilizes SEM software to provide meaningful examples. Each chapter also
features an outline, key concepts, a summary, numerous examples from
a variety of disciplines, tables, and figures, including path diagrams, to
assist with conceptual understanding. Chapters with examples follow the
conceptual sequence of SEM steps known as model specification, identification,
estimation, testing, and modification.
The book now uses LISREL 8.8 student version to make the software and
examples readily available to readers. Please be aware that the student
version, although free, does not contain all of the functional features as a
full licensed version. Given the advances in SEM software over the past
decade, you should expect updates and patches of this software package
and therefore become familiar with any new features as well as explore the
excellent library of examples and help materials. The LISREL 8.8 student
version is an easy-to-use Windows PC based program with pull-down
menus, dialog boxes, and drawing tools. To access the program, and/or
if you’re a Mac user and are interested in learning about Mac availability,
please check with Scientific Software (http://www.ssicentral.com). There
is also a hotlink to the Scientific Software site from the book page for A
Beginner’s Guide to Structural Equation Modeling, 3rd edition on the Textbook
Resources tab at www.psypress.com.
The SEM model examples in the book do not require complicated programming
skills nor does the reader need an advanced understanding of
statistics and matrix algebra to understand the model applications. We have
provided a chapter on the matrix approach to SEM as well as an appendix
on matrix operations for the interested reader. We encourage the understanding
of the matrices used in SEM models, especially for some of the
more advanced SEM models you will encounter in the research literature.