Structural Equation Modeling
Foundations and Extensions

Second Edition

David Kaplan

Volume: 10

Series:
Advanced Quantitative Techniques in the Social Sciences

Courses:
Intermediate/Advanced Statistics | Quantitative Data Analysis | Structural Equation Modeling, Hierarchical Linear Modeling, & Multilevel Modeling

July 2008 | 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.

Available Formats

ISBN: 9781412916240	Hardcover	Suggested Retail Price: $151.00	Bookstore Price: $120.80
ISBN: 9781483342597	Electronic Version	Suggested Retail Price: $101.00	Bookstore Price: $80.80

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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

It provides a great foundation to SEM in a way that students will understand

Dr Kevin Masick

Psychology Dept, Iona College

August 24, 2010

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This book is too technical for my students. It is not user friendly.

Valery Chirkov

Psychology Dept, Univ of Saskatchewan

April 26, 2010

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This is a very up to date guide to structural equation modeling with a good balance of technical statistical aspects and practical applications. In clinical psychology these tools to approach multiple and complex data sets become more and more important.

Professor Matthias Schwannauer

Division of Clinical & Health Psychology, Edinburgh University

December 22, 2009

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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.