Structural Equation Modeling Applications Using Mplus

• Main Authors: Wang, Jichuan, Wang, Xiaoqian
• Format: eBook Book
• Language: English
• Published: Oxford Wiley 2012; S.l. John Wiley; Higher Education Press; John Wiley & Sons, Incorporated; Wiley-Blackwell
• Edition: 3. Aufl.
• Series: Wiley series in probability and statistics
• Subjects: Data processing; Mplus; Multivariate analysis; Multivariate analysis > Data processing; Social sciences; Social sciences > Statistical methods > Data processing; Statistical methods; Structural equation modeling; Structural equation modeling > Data processing; Technik; Wissen Statistik
• ISBN: 1119978297; 9781119978299; 111835625X; 9781118356258; 9781118356302; 1118356306
• DOI: 10.1002/9781118356258

A reference guide for applications of SEM using MplusStructural 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 Mplusresults.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.