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 ...
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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.
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