A Regression-Based Approach

Hardcovere-bookprint + e-book

Hardcover**$75.00**

pre-orderJanuary 14, 2022

ISBN 9781462549030

Price: 732 Pages

Size: 7" x 10"

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.

This title is part of the Methodology in the Social Sciences Series, edited by Todd D. Little, PhD.

“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.”

“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.”

“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.”

“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.”

“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.”

“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.”

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 *X*s or *Y*s: 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

Previous editions published by Guilford:

Second Edition, © 2018

ISBN: 9781462534654

First Edition, © 2013

ISBN: 9781609182304

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.