Overview

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 [ital]X[/ital] and a mediator [ital]M[/ital] in a mediation analysis, and how to test this assumption in a new PROCESS feature.

Full Product Details

Author:   Andrew F. Hayes ,  Maureen Hall ,  Rachel Lella ,  Diane Lapp
Publisher:   Guilford Publications
Imprint:   Guilford Press
Edition:   3rd edition
Weight:   1.400kg
ISBN:  

9781462549030

ISBN 10:   1462549039
Pages:   732
Publication Date:   11 February 2022
Audience:   College/higher education ,  General/trade ,  Tertiary & Higher Education ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   In stock  
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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

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/m-/now available for every example in the book/m-/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-

Author Information

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.

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