Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling

Multiple Regression and Beyond provides a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with related analyses. By emphasizing the concepts and purposes of MR rather than the derivation and calculation of formulas, this book presents the material in a clearer and more accessible way. This approach not only covers essential coursework but also makes it more approachable for students, increasing the likelihood that they will conduct research using MR or SEM effectively and wisely. This book covers both MR and SEM, explaining their relevance to each other. It also includes path analysis, confirmatory factor analysis, and latent growth modeling, incorporating real-world research examples throughout the chapters and end-of-chapter exercises. Figures and tables are used extensively to illustrate key concepts and techniques. This new edition includes: New sections on quantile regression, statistical suppression, contrast coding, and random intercept panel models Support for the statistical program R and the R package lavaan in the text and on the website (www.tzkeith.com) New examples and exercises Updated instructor and student online resources (www.tzkeith.com)

Table of Contents:
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