Structural Equation Modeling Using R/SAS

There has been considerable attention to making the methodologies of structural equation modeling available to researchers, practitioners, and students along with commonly used software. Structural Equation Modelling Using R/SAS aims to bring it all together to provide a concise point-of-reference.

Autorentext

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.

Inhalt

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