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Basic and Advanced Structural Equation Models for Medical and Behavioural Sciences 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. This book takes a Bayesian approach to SEMs allowing 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.
About the authors xiii
1 Introduction 1
2 Basic concepts and applications of structural equation models 16
3 Bayesian methods for estimating structural equation models 34
4 Bayesian model comparison and model checking 64
5 Practical structural equation models 86
6 Structural equation models with hierarchical and multisample data 130
7 Mixture structural equation models 162
8 Structural equation modeling for latent curve models 196
9 Longitudinal structural equation models 224
10 Semiparametric structural equation models with continuous variables 247
11 Structural equation models with mixed continuous and unordered categorical variables 271
12 Structural equation models with nonparametric structural equations 306
13 Transformation structural equation models 341
14 Conclusion 358
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