3rd Edition
Multiple Regression and Beyond An Introduction to Multiple Regression and Structural Equation Modeling
Companion Website materials: https://tzkeith.com/
Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely.
This book:
• Covers both MR and SEM, while explaining their relevance to one another
• Includes path analysis, confirmatory factor analysis, and latent growth modeling
• Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises
• Extensive use of figures and tables providing examples and illustrating key concepts and techniques
New to this edition:
• New chapter on mediation, moderation, and common cause
• New chapter on the analysis of interactions with latent variables and multilevel SEM
• Expanded coverage of advanced SEM techniques in chapters 18 through 22
• International case studies and examples
• Updated instructor and student online resources
Preface
Part I: Multiple Regression
Chapter 1: Simple Bivariate Regression
Chapter 2: Multiple Regression: Introduction
Chapter 3: Multiple Regression: More Depth
Chapter 4: Three and More Independent Variables and Related Issues
Chapter 5: Three Types of Multiple Regression
Chapter 6: Analysis of Categorical Variables
Chapter 7: Regression with Categorical and Continuous Variables
Chapter 8: Testing for Interactions and Curves with Continuous Variables
Chapter 9: Mediation, Moderation, and Common Cause
Chapter 10: Multiple Regression: Summary, Assumptions, Diagnostics, Power, and Problems
Chapter 11: Related Methods: Logistic Regression and Multilevel Modeling
Part II: Beyond Multiple Regression: Structural Equation Modeling
Chapter 12: Path Modeling: Structural Equation Modeling with Measured Variables
Chapter 13: Path Analysis: Assumptions and Dangers
Chapter 14: Analyzing Path Models Using SEM Programs
Chapter 15: Error: The Scourge of Research
Chapter 16: Confirmatory Factor Analysis I
Chapter 17: Putting It All Together: Introduction to Latent Variable SEM
Chapter 18: Latent Variable Models II: Multigroup Models, Panel Models, Dangers & Assumptions
Chapter 19: Latent Means In SEM
Chapter 20: Confirmatory Factor Analysis II: Invariance and Latent Means
Chapter 21: Latent Growth Models
Chapter 22: Latent Variable Interactions and Multilevel Models In SEM
Chapter 23: Summary: Path Analysis, CFA, SEM, Mean Structures, and Latent Growth Models
Appendices
Appendix A: Data Files.
Appendix B: Review of Basic Statistics Concepts
Appendix C: Partial and Semipartial Correlation
Appendix D: Symbols Used in This Book
Appendix E: Useful Formulae
Biography
Timothy Z. Keith is Professor of Educational Psychology at the University of Texas, Austin. His research is focused on the nature and measurement of intelligence, including the validity of tests of intelligence and the theories from which they are drawn. His research has been recognized with awards from the three major journals in school psychology, and he was awarded the senior scientist distinction by the School Psychology division of APA.
