Handbook of Latent Variable and Related Models, Volume 1

Preface
About the Authors

1. Covariance Structure Models for Maximal Reliability of Unit-weighted Composites (Peter M. Bentler)
2. Advances in Analysis of Mean and Covariance Structure When Data are Incomplete (Mortaza Jamshidian, Matthew Mata)
3. Rotation Algorithms: From Beginning to End (Robert I. Jennrich)
4. Selection of Manifest Variables (Yutaka Kano)
5. Bayesian Analysis of Mixtures Structural Equation Models with Missing Data (Sik-Yum Lee)
6. Local Influence Analysis for Latent Variable Models with Nonignorable Missing Responses (Bin Lu, Xin-Yuan Song, Sik-Yum Lee, Fernand Mac-Moune Lai)
7. Goodness-of-fit Measures for Latent Variable Models for Binary Data (D. Mavridis, Irini Moustaki, Martin Knott)
8. Bayesian Structural Equation Modeling (Jesus Palomo, David B. Dunson, Ken Bollen)
9. The Analysis of Structural Equation Model with Ranking Data using Mx (Wai-Yin Poon)
10. Multilevel Structural Equation Modeling (Sophia Rable-Hesketh, Anders Skrondal, Xiaohui Zheng)
11. Statistical Inference of Moment Structure (Alexander Shapiro)
12. Meta-Analysis and Latent Variables Models for Binary Data (Jian-Qing Shi)
13. Analysis of Multisample Structural Equation Models with Applications to Quality of Life Data (Xin-Yuan Song)
14. The Set of Feasible Solutions for Reliability and Factor Analysis (Jos M.F. ten Berge, Gregor Soèan)
15. Nonlinear Structural Equation Modeling as a Statistical Method (Melanie M. Wall, Yasuo Amemiya)
16. Matrix Methods and Their Applications to Factor Analysis (Haruo Yanai, Yoshio Takane)
17. Robust Procedures in Structural Equation Modeling (Ke-Hai Yuan, Peter M. Bentler)
18. Stochastic Approximation Algorithms for Estimation of Spatial Mixed Models (Hongtu Zhu, Faming Liang, Minggao Gu, Bradley Peterson)

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.

• Covers a wide class of important models
• Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data
• Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.
• Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Primary Market(s) Psychology, sociology Education

Secondary Market(s) Business, Biomedical