1st Edition
Structural Equation Modeling Using R/SAS A Step-by-Step Approach with Real Data Analysis
1. Linear Regression to Path Analysis 2. Latent Variables - Confirmatory Factor Analysis 3. Mediation Analysis 4. Structural Equation Modeling with Non-Normal Data 5. Structural Equation Modeling with Categorical Data 6. Multi-Group Data Analysis: Continuous Data 7. Multi-Group Data Analysis: Categorical Data 8. Pain-Related Disability for People with Temporomandibular Disorder: Full Structural Equation Modeling 9. Breast-Cancer Post-Surgery Assessment—Latent Growth-Curve Modeling 10. Full Longitudinal Mediation Modeling 11. Multi-Level Structural Equation Modeling 12. Sample Size Determination and Power Analysis
Biography
Ding-Geng Chen, Ph.D. Professor and Executive Director in Biostatistics College of Health Solutions Arizona State University, USA.
Yiu-Fai Yung, Ph.D. Senior Manager, Advanced Analytics R & D, SAS Institute Inc.
"In sum, this book is an essential read for practitioners and students who seek to use SEM in their research. It bridges the gap between theory and practice in a manner that is both comprehensive and understandable. The structured layout, practical examples with statistical code in R and SAS, and depth of coverage make this book a valuable asset in the field of SEM."
Lifeng Lin, University of Arizona, U.S.A, Journal of the American Statistical Association, Feburary 2024.
