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. Chapter 5: Three Types of MR. Chapter 6: Analysis of categorical variables. Chapter 7: Categorical & continuous variables.

Chapter 8: Continuous variables: Interactions & curves. Chapter 9: Multiple regression: Summary, further study, and problems. Chapter 10: Related methods: Logistic regression and multilevel modeling. Part II: Beyond Multiple Regression: Structural Equation Modeling. Chapter 11: Path modeling: Structural equation modeling with measured variables. Chapter 12: Path analysis: Dangers and assumptions. Chapter 13: Analyzing path models using SEM programs. Chapter 14: Error: The scourge of research.

Chapter 15: Confirmatory factor analysis I. Chapter 16: Putting it all together: Introduction to latent variable SEM. Chapter 17: Latent variable models: More advanced topics. Chapter 18: Latent means in SEM. Chapter 19: Confirmatory factor analysis II: Invariance and latent means. Chapter 20: Latent growth models. Chapter 21: Summary: Path analysis, CFA, SEM, and latent growth models. Appendices.

Appendix A: Data files. Appendices B: Review of basic statistics concepts. Appendix C: Partial and semipartial correlation.