Principles and Practice of Structural Equation Modeling

Methodology in the Social Sciences

by Rex B. Kline

This bestselling text provides a balance between the technical and practical aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM. The companion Web page offers downloadable syntax, data, and output files for each detailed example for EQS, LISREL, and Mplus, allowing readers to view the results of the same analysis generated by three different computer tools.

 New to This Edition*Thoroughly revised and restructured to follow the phases of most SEM analyses.*Syntax, data, and output files for all detailed research examples are now provided online.*Chapter on computer tools.*Exercises with answers, which support self-study.*Topic boxes on specialized issues, such as dealing with problems in the analysis; the assessment of construct measurement reliability; and more.*Updated coverage of a more rigorous approach to hypothesis and model testing; the evaluation of measurement invariance; and more.
*”Troublesome” examples have been added to provide a context for discussing how to handle various problems that can crop up in SEM analyses.

Genres: Nonfiction, Textbooks, Research Methods, Psychology, Reference, Research, Logic

427 pages, Paperback

First published May 27, 1998

Reviews

[Terran M · Rating 5 out of 5]

This is the correct first book to read on causal inference. It covers structural equation modeling (SEM), confirmatory factor analysis (CFA), and Pearl's structured causal modeling (SCM). Adequate preparation for understanding this book would be a basic treatment of multivariate regression, such as Gelman and Hill. Introduction to Statistical Learning would also be sufficient. If you want to really understand confirmatory factor analysis, you should probably already know something about factor analysis as well; I liked Gorsuch.

Although this book claims to cover various software packages, the treatment is cursory and the code examples (online) are mostly uncommented; don't expect to really learn how to use the software from this book. Read this book for the principles and then also read the software manual for whatever tool you're going to use.

Ironically, this book, whose title claims to be about SEM only, actually covers most of modern causal inference, whereas Pearl's book, with the grand title "Causality", covers only his own narrow work. This is definitely the one you want.

[Fahed Al Kerdi · Rating 5 out of 5]

Great book to cite some information about the validity and the reliability of the research instrument.

[Sam · Rating 4 out of 5]

Excellent SEM book for students/academics. Provides detailed explanations of the consensus (and controversy) of state of the art structural equation modeling techniques. Kline's book uses plain language to communicate complex issues in applying SEM to research questions. Very helpful in answering reviewers/referees questions in the publication process. Minus 1 star for lack of MPLUS syntax addressing model comparisons.

[Jared · Rating 5 out of 5]

Yeah that's right.
I'm actually really excited to re-read this book. This guy is a great writer when it comes to this stuff. I think this is a wonderfully powerful tool for gaining insight into how the world works.
This is where stats is going. If you're getting a degree in this stuff, read this.

[Erin · Rating 4 out of 5]

Fantastix explanation of theory, assumptions, and technique.

[Robyn · Rating 5 out of 5]

Very clear and easy to follow. A great textbook for introductory SEM coursework.

[Desiree Leboeuf-davis · Rating 5 out of 5]

Very informative and understandable for non-mathematician folks.