Multiple Regression and Beyond : An Introduction to Multiple Regression and Structural Equation Modeling (Paperback, 4 ed)

Preface
Notes for the Fourth Edition
Acknowledgments

Part I: Multiple Regression
Chapter 1: Simple bivariate regression
Chapter 2: Multiple regression: Introduction
Chapter 3: Multiple regression: More detail
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, common cause, and suppression
Chapter 10: Multiple regression: Summary, assumptions, diagnostics, power, and problems
Chapter 11: Related methods: Quantile regression, 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
Information Classification: General
Chapter 18: Latent variable models II: Single indicators, correlated errors, 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 modeling in SEM
Chapter 23: Summary: Path analysis, CFA, SEM, mean structures, and latent growth models
Appendices
Appendix A: Data files and statistical program notes
Appendices B: Review of basic statistics concepts
Appendix C: Partial and semipartial correlation
Appendix D: Symbols used in this book
Appendix E: Useful formulae

Reference
Author index
Subject index