14. Conclusion

  1. Xin-Yuan Song and
  2. Sik-Yum Lee

Published Online: 18 JUL 2012

DOI: 10.1002/9781118358887.ch14

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

How to Cite

Song, X.-Y. and Lee, S.-Y. (2012) Conclusion, in Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118358887.ch14

Author Information

  1. Department of Statistics, The Chinese University of Hong Kong

Publication History

  1. Published Online: 18 JUL 2012
  2. Published Print: 24 AUG 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470669525

Online ISBN: 9781118358887

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Keywords:

  • Bayesian approach;
  • Markov chain Monte Carlo (MCMC) methods;
  • structural equation models (SEMs)

Summary

This is the conclusion chapter of this book, introduces the Bayesian approach to analyze various types of structural equation models. First, it incorporates genuine prior information and/or knowledge in the analysis so that better results are achieved. Secondly, the book provides more reliable results with moderate sample sizes. Third, it produces better estimates of latent variables than the classical methods. Fourth, the model comparison statistics, such as the Bayes factor and deviance information criterion, provide more reasonable and flexible tools than the classical likelihood ratio test in the maximum likelihood approach. Fifth, and finally, when coupled with data augmentation and MCMC methods, it can be efficiently and effectively applied to handle almost all complex models and/or data structures in substantive research in many fields.

Controlled Vocabulary Terms

Bayes estimator; Bayesian inference; Markov chain Monte Carlo estimation; non-parametric bayesian methods