Structural Equation Modeling: Foundations and Extensions (Advanced Quantitative Techniques in the Social Sciences Series)
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Overview
“Wise social scientists will make a beeline for the 2nd edition of David Kaplan’s classic text. An essential read for students and professionals who want to move beyond cookbook presentations with checklists towards deeper understanding and thoughtful application.”
—Judith D. Singer, Harvard University
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.
Thoroughly revised to address recent developments, this new edition includes:
- 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.
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.
Synopsis
Thoroughly revised to address the recent developments that continue to shape the use of structural equation modeling (SEM) in the social and behavioural sciences, the Second Edition of Structural Equation Modeling author has restructured the book into three defined sections:
- the foundations of SEM, including path analysis and factor analysis
- traditional SEM for continuous latent variables, including assumption issues as well as latent growth curve modeling for continuous growth factors
- SEM for categorical latent variables, including latent class analysis, Markov models (latent and mixed latent), and growth mixture modeling.
Through the use of detailed, empirical examples, Kaplan demonstrates how SEM can provide a unique lens on the problems social and behavioural scientists face. The book has been enhanced with certain features that will guide the student and researcher through the foundations and critical assumptions of SEM.