<p><b>This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables.</b></p><p><i>Basic and Advanced Bayesian Structural Equation Model</i><i>ing </i>introduces basic and advanced SEM
Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences
β Scribed by Xin?Yuan Song, Sik?Yum Lee(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)
- Publisher
- John Wiley & Sons, Ltd
- Year
- 2012
- Tongue
- English
- Leaves
- 391
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored.
Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing.
Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.
Content:Chapter 1 Introduction (pages 1β15):
Chapter 2 Basic Concepts and Applications of Structural Equation Models (pages 16β33):
Chapter 3 Bayesian Methods for Estimating Structural Equation Models (pages 34β63):
Chapter 4 Bayesian Model Comparison and Model Checking (pages 64β85):
Chapter 5 Practical Structural Equation Models (pages 86β129):
Chapter 6 Structural Equation Models with Hierarchical and Multisample Data (pages 130β161):
Chapter 7 Mixture Structural Equation Models (pages 162β195):
Chapter 8 Structural Equation Modeling for Latent Curve Models (pages 196β223):
Chapter 9 Longitudinal Structural Equation Models (pages 224β246):
Chapter 10 Semiparametric Structural Equation Models with Continuous Variables (pages 247β270):
Chapter 11 Structural Equation Models with Mixed Continuous and Unordered Categorical Variables (pages 271β305):
Chapter 12 Structural Equation Models with Nonparametric Structural Equations (pages 306β340):
Chapter 13 Transformation Structural Equation Models (pages 341β357):
Chapter 14 Conclusion (pages 358β360):
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