Table of Contents

Preface 1. Definition of SEM 2. Types of SEM 3. Benefits of SEM 4. Drawbacks of SEM 5. Steps in Structural Equation Modelling 6. Model Specification: Path Diagram in SEM 7. Model Identification 8. Model Estimation 9. Model Fit Evaluation 10. Model Modification 11. Model Cross-Validation 12. Parameter Testing 13. Reduced-Form Version of SEM 14. Multiple Indicators Multiple Causes Model of SEM 15. Practical Issues to Consider when Implementing SEM 16. Review Questions 17. Enlightening Questions on SEM 18. Applied Structural Equation Modelling Using R 19. Applied Structural Equation Modelling using STATA Appendix Bibliography About the Author

About the Author

Indranarain Ramlall, University of Mauritius, Mauritius

Reviews

Known as causal models with a conspicuous presence in the field of consumer psychology, structural equation model (SEM) allows complex modelling of correlated multivariate data in order to sieve out their interrelationships among observed and latent variables. This graduate textbook introduces the four main types of structural equation models, the path diagram for model specification, model identification, model estimation, model fit evaluation, and practical issues to consider when implementing models. Example code demonstrates applied structural equation modelling using R and STATA. -- Annotation (c)2017 * (protoview.com) *