Structural Equation Modeling: A Bayesian Approach

by Sik-Yum Lee

***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Structural Equation A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, statistics, biostatistics, business, education, medicine, psychology, public health and social science.

464 pages, Hardcover

First published March 23, 2007

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[Gwern · Rating 3 out of 5]

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