Bayesian Model Comparison of Structural Equation Models

Part of the Lecture Notes in Statistics book series (LNS, volume 192)


Structural equation modeling is a multivariate method for establishing meaningful models to investigate the relationships of some latent (causal) and manifest (control) variables with other variables. In the past quarter of a century, it has drawn a great deal of attention in psychometrics and sociometrics, both in terms of theoretical developments and practical applications (see Bentler and Wu, 2002; Bollen, 1989; Jöreskog and Sörbom, 1996; Lee, 2007). Although not to the extent that they have been used in behavioral, educational, and social sciences, structural equation models (SEMs) have been widely used in public health, biological, and medical research (see Bentler and Stein, 1992; Liu et al. 2005; Pugesek et al., 2003 and references therein). A review of the basic SEM with applicants to environmental epidemiology has been given by Sanchez et al. (2005).


Bayesian Information Criterion Path Sampling Deviance Information Criterion Manifest Variable Full Conditional Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research is fully supported by two grants (CUHK 404507 and 450607) from the Research Grant Council of the Hong Kong Special Administrative Region, and a direct grant from the Chinese University of Hong Kong (Project ID 2060278). The authors are indebted to Dr. John C. K. Lee, Faculty of Education, The Chinese University of Hong Kong, for providing the data in the application.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of StatisticsChinese University of Hong KongShatinHong Kong

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