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Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach 2ed

by Andrew F Hayes Guilford Publications
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Hbk 692 pages
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Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website (>, along with links to download PROCESS.


New to This Edition

*Chapters on using each type of analysis with multicategorical antecedent variables.

*Example analyses using PROCESS v3, with annotated outputs throughout the book.

*More tips and advice, including new or revised discussions of formally testing moderation of a mechanism using the index of moderated mediation; effect size in mediation analysis; comparing conditional effects in models with more than one moderator; using R code for visualizing interactions; distinguishing between testing interaction and probing it; and more.

*Rewritten Appendix A, which provides the only documentation of PROCESS v3, including 13 new preprogrammed models that combine moderation with serial mediation or parallel and serial mediation.

*Appendix B, describing how to create customized models in PROCESS v3 or edit preprogrammed models.


AUDIENCE: Applied researchers in psychology, human development, education, sociology, public health, communication, and management; graduate students and instructors.



COURSE USE: Will serve as a core book or supplement in graduate courses on quantitative data analysis, regression, or multivariate analysis.


I. Fundamentals
1. Introduction
1.1. A Scientist in Training
1.2. Questions of Whether, If, How, and When
1.3. Conditional Process Analysis
1.4. Correlation, Causality, and Statistical Modeling
1.5. Statistical and Conceptual Diagrams, and Antecedent and Consequent Variables
1.6. Statistical Software
1.7. Overview of This Book
1.8. Chapter Summary
2. Fundamentals of Linear Regression Analysis
2.1. Correlation and Prediction
2.2. The Simple Linear Regression Model
2.3. Alternative Explanations for Association
2.4. Multiple Linear Regression
2.5. Measures of Model Fit
2.6. Statistical Inference
2.7. Multicategorical Antecedent Variables
2.8. Assumptions for Interpretation and Statistical Inference
2.9. Chapter Summary
II. Mediation Analysis
3. The Simple Mediation Model
3.1. The Simple Mediation Model
3.2. Estimation of the Direct, Indirect, and Total Effects of X
3.3. Example with Dichotomous X: The Influence of Presumed Media Influence
3.4. Statistical Inference
3.5. An Example with Continuous X: Economic Stress among Small-Business Owners
3.6. Chapter Summary
4. Causal Steps, Confounding, and Causal Order
4.1. What about Baron and Kenny?
4.2. Confounding and Causal Order
4.3. Effect Size
4.4. Statistical Power
4.5. Multiple Xs or Ys: Analyze Separately or Simultaneously?
4.6. Chapter Summary
5. More Than One Mediator
5.1. The Parallel Multiple Mediator Model
5.2. Example Using the Presumed Media Influence Study
5.3. Statistical Inference
5.4. The Serial Multiple Mediator Model
5.5. Models With Parallel and Serial Mediation Properties
5.6. Complementarity and Competition among Mediators
5.7. Chapter Summary
6. Mediation Analysis with a Multicategorical Antecedent X
6.1. Relative Total, Direct, and Indirect Effects
6.2. An Example: Sex Discrimination in the Workplace
6.3. Using a Different Group Coding System
6.4. Some Miscellaneous Issues
6.5. Chapter Summary
III. Moderation Analysis
7. Fundamentals of Moderation Analysis
7.1. Conditional and Unconditional Effects
7.2. An Example: Climate Change Disasters and Humanitarianism
7.3. Visualizing Moderation
7.4. Probing an Interaction
7.5. The Difference between Testing for Moderation and Probing It
7.6. Artificial Categorization and Subgroups Analysis
7.7. Chapter Summary
8. Extending the Fundamentals of Moderation Analysis
8.1. Moderation with a Dichotomous Moderator
8.2. Interaction between Two Quantitative Variables
8.3. Hierarchical versus Simultaneous Entry
8.4. The Equivalence between Moderated Regression Analysis and a 2 × 2 Factorial Analysis of Variance
8.5. Chapter Summary
9. Some Myths and Further Extensions of Moderation Analysis
9.1. Truths and Myths about Mean Centering
9.2. The Estimation and Interpretation of Standardized Regression Coefficients in a Moderation Analysis
9.3. A Caution on Manual Centering and Standardization
9.4. More than One Moderator
9.5. Comparing Conditional Effects
9.6. Chapter Summary
10. Multicategorical Focal Antecedents and Moderators
10.1. Moderation of the Effect of a Multicategorical Antecedent Variable
10.2. An Example from the Sex Discrimination in the Work Place Study
10.3. Visualizing the Model
10.4. Probing the Interaction
10.5. When the Moderator is Multicategorical
10.6. Using a Different Coding System
10.7. Chapter Summary
IV. Conditional Process Analysis
11. Fundamentals of Conditional Process Analysis
11.1. Examples of Conditional Process Models in the Literature
11.2. Conditional Direct and Indirect Effects
11.3. Example: Hiding Your Feelings from Your Work Team
11.4. Estimation of a Conditional Process Model using PROCESS
11.5. Quantifying and Visualizing (Conditional) Indirect and Direct Effects
11.6. Statistical Inference
11.7. Chapter Summary
12. Further Examples of Conditional Process Analysis
12.1. Revisiting the Disaster Framing Study
12.2. Moderation of the Direct and Indirect Effects in a Conditional Process Model
12.3. Statistical Inference
12.4. Mediated Moderation
12.5. Chapter Summary
13. Conditional Process Analysis with a Multicategorical Antecedent
13.1. Revisiting Sexual Discrimination in the Work Place
13.2. Looking at the Components of the Indirect Effect of X
13.3. Relative Conditional Indirect Effects
13.4. Testing and Probing Moderation of Mediation
13.5. Relative Conditional Direct Effects
13.6. Putting It All Together
13.7. Chapter Summary
V. Miscellanea
14. Miscellaneous Topics and Some Frequently Asked Questions
14.1. A Strategy for Approaching a Conditional Process Analysis
14.2. How Do I Write about This?
14.3. Should I Use Structural Equation Modeling Instead of Regression Analysis?
14.4. The Pitfalls of Subgroups Analysis
14.5. Can a Variable Simultaneously Mediate and Moderate Another Variable’s Effect?
14.6. Interaction between X and M in Mediation Analysis
14.7. Repeated Measures Designs
14.8. Dichotomous, Ordinal, Count, and Survival Outcomes
14.9. Chapter Summary
Appendix A. Using PROCESS
Appendix B. Constructing and Customizing Models in PROCESS
Appendix C. Monte Carlo Confidence Intervals in SPSS and SAS
About the Author

“The book is very readable and conversational, providing many interesting and useful examples….I found this to be a very nice book that is readable enough for the intermediate statistics user but with enough technical detail to appeal to advanced users as well....This book would make an excellent textbook for an advanced graduate-level multiple regression course, or just a great resource for the interested reader.”


Journal of Educational Measurement (on the first edition)



“This book elegantly presents both the basic and advanced issues of mediation and moderation analysis…It will be beneficial for graduate students and applied researchers who are interested in causal mechanisms using linear models….[T]his is a very good textbook for applied researchers in social sciences. It covers mediation and moderation analysis using regression techniques quite nicely....I think this book could be very useful for both preliminary and advanced readers.”


Journal of the American Statistical Association (on the first edition)



“This book provides clear instruction that is accessible to graduate students while also useful to seasoned researchers looking to expand their skills for more complex regression-based analyses. The second edition provides increased clarity in interpreting PROCESS output and documents PROCESS v3, which allows for great flexibility in analyzing models. Other useful developments in the second edition include chapters on multicategorical variables, incorporation of the index of moderated mediation, and the appendix of instructions on how to customize PROCESS for models not covered by the templates. Hayes’s approach is cutting edge in both philosophy and pragmatics. I've used the first edition extensively as a course text as well as in my own research, and am excited to move to the second edition.”


—Elizabeth J. Kiel, PhD, Department of Psychology, Miami University



“Using lucid prose and abundant, worked-through examples, Hayes walks readers through the promise and potential pitfalls of two of the most essential—yet convoluted—tasks in social science research. Novices will find this book to be a thorough, accessible description of ordinary least squares regression and a smart tutorial on mediation and moderation, but it is also much more. Any seasoned researcher who has slogged through the arcane computation and agonizing decision making related to the estimation and interpretation of direct and indirect effects, or the visualization and presentation of interactions, will find this volume (with the accompanying PROCESS macro) to be a veritable Swiss Army knife, and will return to it time and time again.”


—Jeffrey G. Parker, PhD, Department of Psychology, University of Alabama



“This text is a wonderful combination of traditional mediation and moderation using regression and extensions into more complex variations. Coverage is clear and thorough—perfect for intermediate to advanced regression learners. Updates in the second edition include a new chapter with answers to many very important and common questions, which will be extremely helpful to learners. I can't wait to use this second edition with my students.”


—Jocelyn H. Bolin, PhD, Department of Educational Psychology, Ball State University



“This second edition is a welcome addition to advanced regression books that can be used in doctoral courses in the social sciences or by social science researchers. Hayes maintains his usual level of clarity while adding coverage of such important topics as multicategorical variables for mediation, moderation, and conditional process models. Enhanced presentation of tabular materials, coupled with new plots, add to the reader’s understanding of analyses. Incorporation of R syntax at points in the book is great, as many researchers turn to R for its open access and improved graphics capabilities. I loved the first edition for my first-year doctoral course, and will use the second edition in its place.”


—Ramona L. Paetzold, DBA, Department of Management, Texas A&M University



“Since I began using the first edition of this text in my graduate statistics classes in 2014, the number of theses and dissertations that include mediation and/or moderation analysis in our department has increased dramatically. Valuable new material in the second edition includes 13 new models, including models with categorical variables and models with both parallel and serial mediation, as well as the recently developed index of moderated mediation. My copy of the first edition is filled with my annotations on the examples of PROCESS output—in the second edition, Hayes has provided useful annotations of his own. I highly recommend this book for statistics classes that include OLS mediation and moderation. It is also a terrific resource for researchers wishing to keep up with advances in moderation and mediation analysis.”


—Karl L. Wuensch, PhD, Department of Psychology, East Carolina University

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, and he is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition, and Statistical Methods for Communication Science, as well as coauthor, with Richard B. Darlington, of Regression Analysis and Linear Models. Dr. Hayes teaches data analysis, primarily at the graduate level, and frequently conducts workshops on statistical analysis throughout the world. His website is