Structural Equation Modeling: Applications Using Mplus [Kõva köide]

"Focuses on the methods and practical aspects of SEM models using Mplus"--

Jichuan (children's medicine, George Washington U.) and Xiaoqian, with a Chinese company, explain structural equation modeling as it is increasingly being used in social and health sciences. The approach has several advantages over others, they say, but is not used as much as it could be if it were better known. They cover confirmatory factor analysis, structural equations with latent variable, latent growth models for longitudinal data analysis, multi-group modeling, mixture modeling, and sample size for structural equation modeling. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

A reference guide for applications of SEM using Mplus

Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results.

Key features:

• Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus.
• Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes.
• Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplus results.
• Explores different methods for sample size estimate and statistical power analysis for SEM.

By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.