Principles and Practice of Structural Equation Modeling

Methodology in the Social Sciences

by Rex B. Kline

Significantly revised, the fifth edition of the most complete, accessible text now covers all three approaches to structural equation modeling (SEM)--covariance-based SEM, nonparametric SEM (Pearl’s structural causal model), and composite SEM (partial least squares path modeling) . With increased emphasis on freely available software tools such as the R lavaan package, the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM--what to know, best practices, and pitfalls to avoid. It includes exercises with answers, rules to remember, topic boxes, and new self-tests on significance testing, regression, and psychometrics. The companion website supplies helpful primers on these topics as well as data, syntax, and output for the book's examples, in files that can be opened with any basic text editor.
 
New to This Edition
*Chapters on composite SEM, also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis.
*Coverage of new reporting standards for SEM analyses; piecewise SEM, also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM.
*Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects.
 
Pedagogical Features
*New self-tests of knowledge about background topics (significance testing, regression, and psychometrics) with scoring key and online primers.
*End-of-chapter suggestions for further reading and exercises with answers.
*Troublesome examples from real data, with guidance for handling typical problems in analyses.
*Topic boxes on special issues and boxed rules to remember.
*Website promoting a learn-by-doing approach, including data, extensively annotated syntax, and output files for all the book’s detailed examples.
 

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

494 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.