A Beginner's Guide to Structural Equation Modeling
- Available for pre-order. Item will ship after April 28, 2022
A Beginner’s Guide to Structural Equation Modeling, fifth edition, has been redesigned with consideration of a true beginner in structural equation modeling (SEM) in mind. The book covers introductory through intermediate topics in SEM in more detail than in any previous edition.
All of the chapters that introduce models in SEM have been expanded to include easy-to-follow, step by step guidelines that readers can use when conducting their own SEM analyses. These chapters also include examples of tables to include in results sections that readers may use as templates when writing up the findings from their SEM analyses. The models that are illustrated in the text will allow SEM beginners to conduct, interpret, and write up analyses for observed variable path models to full structural models, up to testing higher order models as well as multiple group modeling techniques. Updated information about methodological research in relevant areas will help students and researchers be more informed readers of SEM research. The checklist of SEM considerations when conducting and reporting SEM analyses is a collective set of requirements that will help improve the rigor of SEM analyses.
This book is intended for true beginners in structural equation modeling and is designed for introductory graduate courses in structural equation modeling taught in psychology, education, business, and the social and healthcare sciences. This book also appeals to researchers and faculty in various disciplines. Prerequisites include correlation and regression methods.
Table of Contents
1. Introduction 2. Data Entry and Editing Issues 3. Correlation and Regression Methods 4. Path Models 5. SEM Basics 6. Factor Analysis 7. Full SEM 8. Extensions of CFA Models 9. Multiple Group (Sample) Models 10. SEM Considerations; Appendix. Introduction to Matrix Algebra Statistical Tables
Tiffany A. Whittaker is an Associate Professor in the Department of Educational Psychology at The University of Texas at Austin, USA, where she teaches courses in structural equation modeling, statistical analysis for experimental data, and advanced statistical modeling.
Randall E. Schumacker is a Professor of Educational Research at The University of Alabama, USA, where he teaches courses in multiple regression, multivariate statistics and structural equation modeling.