With 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. Then Kaplan 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.

About the Author

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.

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