Introduction to Mediation : A Regression-Based Approach book cover
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3rd Edition

Introduction to Mediation
A Regression-Based Approach



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ISBN 9781462549030
February 23, 2022 Forthcoming by Guilford Press
732 Pages

 
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Book Description

Acclaimed for its thorough presentation of mediation, moderation, and conditional process analysis, this book has been updated to reflect the latest developments in PROCESS for SPSS, SAS, and, new to this edition, R. Using the principles of ordinary least squares regression, Andrew F. Hayes illustrates each step in an analysis using diverse examples from published studies, and displays SPSS, SAS, and R code for each example. Procedures are outlined for estimating and interpreting direct, indirect, and conditional effects; probing and visualizing interactions; testing hypotheses about the moderation of mechanisms; and reporting different types of analyses. Readers gain an understanding of the link between statistics and causality, as well as what the data are telling them. The companion website (www.afhayes.com) provides data for all the examples, plus the free PROCESS download.

New to This Edition
*Rewritten Appendix A, which provides the only documentation of PROCESS, including a discussion of the syntax structure of PROCESS for R compared to SPSS and SAS.
*Expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects.
*Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using a new PROCESS option.
*Discussion of a method for comparing the strength of two specific indirect effects that are different in sign.
*Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model.
*Discussion of testing for interaction between a causal antecedent variable X and a mediator M in a mediation analysis, and how to test this assumption in a new PROCESS feature.

Table of Contents

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, Scaling, Confounding, and Causal Order
4.1. What about Baron and Kenny?
4.2. Confounding and Causal Order
4.3. Effect Scaling
4.4. Multiple Xs or Ys: Analyze Separately or Simultaneously?
4.5. 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
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 Fundamental Principles 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 x 2 Factorial Analysis of Variance
8.5. Chapter Summary
9. Some Myths and Additional 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 Workplace 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 Workplace
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. Further Extensions and Complexities
13.8. 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. Power and Sample Size Determination
14.4. Should I Use Structural Equation Modeling Instead of Regression Analysis?
14.5. The Pitfalls of Subgroups Analysis
14.6. Can a Variable Simultaneously Mediate and Moderate Another Variable’s Effect?
14.7. Interaction between X and M in Mediation Analysis
14.8. Repeated Measures Designs
14.9. Dichotomous, Ordinal, Count, and Survival Outcomes
14.10. Chapter Summary
Appendix A. Using PROCESS
Appendix B. Constructing and Customizing Models in PROCESS

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Author(s)

Biography

Andrew F. Hayes, PhD, is Distinguished Research Professor at the Haskayne School of Business at the University of Calgary, Alberta, Canada. 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, Third 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 conducts workshops on statistical moderation and mediation analysis throughout the world. His website is www.afhayes.com.

Reviews

“I know I speak for organizational researchers and graduate students everywhere when I say how much PROCESS, and prior editions of this book, have contributed to making some of the more difficult parts of the research process accessible and fun. I look forward to using the third edition in my own research, and (again) buying a copy for all my graduate students. Adding to the appeal of the third edition are features such as the new code for R users--now available for every example in the book--and techniques to analyze the strength of two specific direct effects that differ in sign. Hayes has made an immense contribution with his continual updates to PROCESS, and shows in his writing and his workshops that he is a gifted teacher.”--Julian Barling, PhD, FRSC, Distinguished University Professor and Borden Chair of Leadership, Smith School of Business, Queen’s University, Canada

"This book would make an excellent companion text to accompany a course on regression analysis that also addresses mediation and moderation, two topics of enormous practical utility. It can also serve as a useful reference for more experienced researchers and methodologists wanting to learn about mediation, moderation, and advanced applications. Reading this book is like taking an immersive workshop on mediation and moderation analysis, with the author right there to explain everything."--Kristopher J. Preacher, PhD, Department of Psychology and Human Development, Peabody College, Vanderbilt University

"I have used this text for several years in my graduate-level statistics classes. It makes the teaching of mediation and moderation much easier, and the associated PROCESS code makes conducting these analyses much less tedious. Colleagues have found this book and PROCESS very helpful in their research endeavors, and several of my students have used PROCESS in their theses and dissertations. The third edition has all of the things I liked about the earlier editions, plus some nice new stuff--the inclusion of R code will be helpful to those who do not have access to SAS or SPSS, and I especially enjoyed the more detailed discussion of unstandardized, standardized, and partially standardized coefficients. I recommend this book without reservation."--Karl L. Wuensch, PhD, Department of Psychology, East Carolina University

"This book is a staple on my bookshelf and a text that I recommend to all my students who are interested in quantitative research. The impressive third edition now includes code and examples for R. Making the incredibly flexible and useful analytic tools of PROCESS available for a free, open-source statistical software program is a huge contribution to the field. This is a most useful book for advanced graduate courses that focus on regression, as well as for faculty.”--Michael D. Broda, PhD, School of Education, Virginia Commonwealth University-