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. It covers a wide class of important models. Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data. It includes illustrative examples with real data sets from business, education, medicine, public health and sociology. It demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.
- Limba : Engleza
- Cuprins : 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
- Data Publicarii : 08 Feb 2007
- Format : Hardback
- Numar pagini : 458
- ISBN : 9780444520449