Structural Equation Modeling: 2000 + jacket, free

"This is a thorough and sophisticated treatment of structural equation modeling (SEM). The book assumes a strong background in statistics and matrix algebra. The author clearly is knowledgeable and has put much thought into the pros and cons of standard applications of SEM. The chapters on multilevel and growth modeling are an excellent feature of the book."

--Rick H. Hoyle, University of Kentucky

Through the use of detailed, empirical examples, this exciting book presents an advanced treatment of the foundations of structural equation modeling (SEM) and demonstrates how SEM can provide a unique lens on problems in the social and behavioral sciences.

The author begins with an introduction to recursive and non-recursive models, estimation, testing, and the problem of measurement in observed variables. Kaplan then explores the issue of group differences in structural models, statistical assumptions in structural modeling (from sampling to missing data and specification error), the assessment of statistical power and model modification in the context of model evaluation, and SEM applied to complex data structures such as those obtained from clustered random sampling. The book concludes with a discussion of recent developments in latent variable growth curve modeling and a critique of the conventional practice of structural modeling in light of recent developments in econometric modeling.

Features/Benefits:

- Shows how SEM can be used to answer substantive questions by weaving a small set of substantive examples throughout the book

- Explains recent developments in structural equation modeling applied to complex sampling, such as multilevel SEM and latent variable growth

- Provides a critique of the standard practice of structural equation modeling throughout the book and discusses an alternative approach in light of recent developments in econometric modeling.

David Kaplan received his Ph.D. in Education from UCLA in 1987. He is now a Professor of Education and (by courtesy) Psychology at the University of Delaware. His research interests are in the development and application of statistical models to problems in educational evaluation and policy analysis. His current program of research concerns the development of dynamic latent continuous and categorical variable models for studying the diffusion of educational innovations.