Principles and Practice of Structural Equation Modeling

Methodology in the Social Sciences

by Rex B. Kline

Designed for researchers and students without an extensive quantitative background, this book offers an informative guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) in psychology and the social sciences. This is an accessible volume which covers introductory techniques, including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods, such as the evaluation of nonlinear effects, the analysis of means in covariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the author offers clear instructions on the preparation and screening of data, common mistakes to avoid, and features of widely used software programs (Amos, EQS, and LISREL). Readers will acquire the skills necessary to begin to use SEM in their own research, and to interpret and critique the use of the method by others, making this a valuable text for students of psychology, communication sciences, education, sociology, and related fields.

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

354 pages, Hardcover

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.