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.