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Structural Equation Modeling
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Structural Equation Modeling
Foundations and Extensions

Second Edition


© 2009 | 272 pages | SAGE Publications, Inc
Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face.

Intended Audience

While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.
 
Preface to the Second Edition
 
1. Historical Foundations of Structural Equation Modeling for Continuous and Categorical Latent Variables
 
2. Path Analysis: Modeling Systems of Structural Equations Among Observed Variables
 
3. Factor Analysis
 
4. Structural Equation Models in Single and Multiple Groups
 
5. Statistical Assumptions Underlying Structural Equation Modeling
 
6. Evaluating and Modifying Structural Equation Models
 
7. Multilevel Structural Equation Modeling
 
8. Latent Growth Curve Modeling
 
9. Structural Models for Categorical and Continuous Latent Variables
 
10. Epilogue: Toward a New Approach to the Practice of Structural Equation Modeling

A must have for those looking forward understanding SEM at the equations level.

Dr Bruno Schivinski
Nottingham Business School, Nottingham Trent University
November 11, 2016

Although this textbook is one of the best in the market for SEM, I found it very complex to use with students. It should be used in combination with other textbooks more focused on the practical application of SEM.

Dr Bruno Schivinski
Nottingham Business School, Nottingham Trent University
October 22, 2016

The book takes a very mathematical approach and is too advanced/unsuitable for most postgraduate courses in the social sciences. It also doesn't provide a practical approach to using SEM or SEM software. The book would be more value and interest to students on mathematical courses or those who want a better understanding of the statistical theory underpinning SEM.

Dr Trevor James
School of Psychology, Newcastle University
May 25, 2016

The book is great, but maybe too sophisticated for master students.

Professor Jost Sieweke
Business Administration , Heinrich Heine University
April 1, 2016

Advanced book for structural equation modeling. I recommend this book to students who already have some experience in this field. It is well written and a great possibility to further gain some insights!

Dr Christian Baccarella
Business School, Friedrich-Alexander-University
December 9, 2015

While David Kaplan did an excellent job in describing the Extensions of SEM, this book is not suited for students starting with SEM.

Dr Sascha Alexander Ruhle
Wirtschaftswissenschaften, Heinrich-Heine-University Dusseldorf
September 30, 2015

Unfortunately the book starts too quickly into technical details without explaining basics such as endogenous and exogenous variables (p.14). It is therefore not suitable as a text book for beginners.

Dr Daniel Stahl
Biostatistics and Computing, King's College London
December 17, 2014

This is a very useful guide to structural equation modelling. I would recommend it as supplemental material for my measurement courses.

Dr Maria Pampaka
Social Science, Univ. of Manchester
August 29, 2014

A classic book well explained and appropriate for an introduction to SEM

Mr Jamel Khenfer
Marketing , Paul Cezanne University Aix-Marseille III
April 24, 2014

Provides great explanation of SEMs

Professor Mary Siegrist
School Of Health Sciences, Ohio University
December 5, 2013

Pages

Key features
NEW TO THIS EDITION:
  • The foundations of SEM, including path analysis and factor analysis.
  • Traditional SEM for continuous latent variables, including latent growth curve modeling for continuous growth factors, and issues in testing assumptions of SEM.
  • SEM for categorical latent variables, including latent class analysis, Markov models (latent and mixed latent), and growth mixture modeling.
  • Philosophical issues in the practice of SEM, including the problem of causal inference.

For instructors

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