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I. FUNDAMENTAL CONCEPTS1. Introduction1.1. A Scientist in Training1.2. Questions of Whether, If, How, and When1.3. Conditional Process Analysis1.4. Correlation, Causality, and Statistical Modeling1.5. Statistical Software1.6. Overview of this Book1.7. Chapter Summary2. Simple Linear Regression 2.1. Correlation and Prediction2.2. The Simple Linear Regression Equation2.3. Statistical Inference2.4. Assumptions for Interpretation and Statistical Inference2.5. Chapter Summary3. Multiple Linear Regression 3.1. The Multiple Linear Regression Equation3.2. Partial Association and Statistical Control3.3. Statistical Inference in Multiple Regression3.4. Statistical and Conceptual Diagrams3.5. Chapter SummaryII. MEDIATION ANALYSIS4. The Simple Mediation Model 4.1. The Simple Mediation Model4.2. Estimation of the Direct, Indirect, and Total Effects of X4.3. Example with Dichotomous X: The Influence of Presumed Media Influence4.4. Statistical Inference4.5. An Example with Continuous X: Economic Stress among Small Business Owners4.6. Chapter Summary5. Multiple Mediator Models5.1. The Parallel Multiple Mediator Model5.2. Example Using the Presumed Media Influence Study5.3. Statistical Inference5.4. The Serial Multiple Mediator Model5.5. Complementarity and Competition among Mediators5.6. OLS Regression versus Structural Equation Modeling5.7. Chapter SummaryIII. MODERATION ANALYSIS6. Miscellaneous Topics in Mediation Analysis 6.1. What About Baron and Kenny?6.2. Confounding and Causal Order6.3. Effect Size6.4. Multiple Xs or Ys: Analyze Separately or Simultaneously?6.5. Reporting a Mediation Analysis6.6. Chapter Summary7. Fundamentals of Moderation Analysis 7.1. Conditional and Unconditional Effects7.2. An Example: Sex Discrimination in the Workplace7.3. Visualizing Moderation7.4. Probing an Interaction7.5. Chapter Summary8. Extending Moderation Analysis Principles 8.1. Moderation Involving a Dichotomous Moderator8.2. Interaction between Two Quantitative Variables8.3. Hierarchical versus Simultaneous Variable Entry8.4. The Equivalence between Moderated Regression Analysis and a 2 x 2 Factorial Analysis of Variance8.5. Chapter Summary9. Miscellaneous Topics in Moderation Analysis 9.1. Truths and Myths about Mean Centering9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis9.3. Artificial Categorization and Subgroups Analysis9.4. More Than One Moderator9.5. Reporting a Moderation Analysis9.6. Chapter SummaryIV. CONDITIONAL PROCESS ANALYSIS10. Conditional Process Analysis 10.1. Examples of Conditional Process Models in the Literature10.2. Conditional Direct and Indirect Effects10.3. Example: Hiding Your Feelings from Your Work Team10.4. Statistical Inference10.5. Conditional Process Analysis in PROCESS10.6. Chapter Summary11. Further Examples of Conditional Process Analysis 11.1. Revisiting the Sexual Discrimination Study11.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model11.3. Visualizing the Direct and Indirect Effects11.4. Mediated Moderation11.5. Chapter Summary12. Miscellaneous Topics in Conditional Process Analysis 12.1. A Strategy for Approaching Your Analysis12.2. Can a Variable Simultaneously Mediate and Moderate Another Variable's Effect?12.3. Comparing Conditional Indirect Effects and a Formal Test of Moderated Mediation12.4. The Pitfalls of Subgroups Analysis12.5. Writing about Conditional Process Modeling12.6. Chapter SummaryAppendix A. Using PROCESS Appendix B. Monte Carlo Confidence Intervals in SPSS and SAS
Andrew F. Hayes, PhD, is Professor of Quantitative Psychology at The Ohio State University. His research and writing on data analysis has been published widely. Dr. Hayes is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis and Statistical Methods for Communication Science, as well as coauthor, with Richard B. Darlington, of Regression Analysis and Linear Models. He teaches data analysis, primarily at the graduate level, and frequently conducts workshops on statistical analysis throughout the world. His website is www.afhayes.com.
"Mediation and moderation are two of the most widely used statistical tools in the social sciences. Students and experienced researchers have been waiting for a clear, engaging, and comprehensive book on these topics for years, but the wait has been worth it--this book is an absolute winner. With his usual clarity, Hayes has written what will become the default resource on mediation and moderation for many years to come."--Andy Field, PhD, School of Psychology, University of Sussex, United Kingdom "Hayes provides an accessible, thorough introduction to the analysis of models containing mediators, moderators, or both. The text is easy to follow and written at a level appropriate for an introductory graduate course on mediation and moderation analysis. The book is also an extremely useful resource for applied researchers interested in analyzing conditional process models. One strength is the inclusion of numerous examples using real data, with step-by-step instructions for analysis of the data and interpretation of the results. This book's largest contribution to the field is its replacement of the confusing terminology of mediated moderation and moderated mediation with the clearer and broader term conditional process model."--Matthew Fritz, PhD, Department of Educational Psychology, University of Nebraska-Lincoln "A welcome contribution. This book's accessible language and diverse set of examples will appeal to a wide variety of substantive researchers looking to explore how or why, and under what conditions, relationships among variables exist. Hayes has a unique ability to effectively communicate technical material to nontechnical audiences. He facilitates application of several cutting-edge statistical models by providing practical, well-oiled machinery for conducting the analyses in practice. I can use this book to enhance my graduate-level mediation class by extending the course to include more coverage on differentiating mediation versus moderation and on conditional process models that simultaneously evaluate both effects together."--Amanda Jane Fairchild, PhD, Department of Psychology, University of South Carolina "This decidedly readable, informative book is perfectly suited for a range of audiences, from the novice graduate student not quite ready for SEM to the advanced statistics instructor. Even the seasoned quantitative methodologist will benefit from Hayes's years of accumulated wisdom as he expertly navigates this burgeoning--and at times inconsistent--literature. This book is particularly well suited for graduate-level courses. Hayes brings conditional process analysis to life with such passion that even the most 'stat-o-phobic' will become convinced that they too can master SPSS (or SAS) process. The thoughtful use of real-life examples, accompanied by SPSS and SAS syntax and output, makes the book highly accessible."--Shelley Brown, PhD, Department of Psychology, Carleton University, Canada