1. What Is a Moderator Variable and Why Should We Care?
Why Should We Study Moderator Variables?
Distinction between Moderator and Mediator Variables
Importance of A Priori Rationale in Investigating Moderating
Effects
Conclusions
2. Moderated Multiple Regression
What Is MMR?
Endorsement of MMR as an Appropriate Technique
Pervasive Use of MMR in the Social Sciences: Literature Review
Conclusions
3. Performing and Interpreting Moderated Multiple Regression
Analysis Using Computer Programs
Research Scenario
Data Set
Conducting an MMR Analysis Using Computer Programs: Two Steps
Output Interpretation
Conclusions
4. Homogeneity of Error Variance Assumption
What Is the Homogeneity of Error Variance Assumption?
Two Distinct Assumptions: Homoscedasticity and Homogeneity of Error
Variance
Is It a Big Deal to Violate the Assumption?
Violation of the Assumption in Published Research
How to Check If the Homogeneity Assumption Is Violated
What to Do When the Homogeneity of Error Variance Assumption Is
Violated
ALTMMR: Computer Program to Check Assumption Compliance and Compute
Alternative Statistics If Needed
Conclusions
5. MMR’s Low-Power Problem
Statistical Inferences and Power
Controversy Over Null Hypothesis Significance Testing
Factors Affecting the Power of All Inferential Tests
Factors Affecting the Power of MMR
Effect Sizes and Power in Published Research
Implications of Small Observed Effect Sizes for Social Science
Research
Conclusions
6. Light at the End of the Tunnel: How to Solve the Low-Power
Problem
How to Minimize the Impact of Factors Affecting the Power of All
Inferential Tests
How to Minimize the Impact of Factors Affecting the Power of
MMR
Conclusions
7. Computing Statistical Power
Usefulness of Computing Statistical Power
Empirically Based Programs
Theory-Based Program
Relative Impact of the Factors Affecting Power
Conclusions
8. Complex MMR Models
MMR Analyses Including a Moderator Variable with More Than Two
Levels
Linear Interactions and Non-linear Effects: Friends or Foes?
Testing and Interpreting Three-Way and Higher-Order Interaction
Effects
Conclusions
9. Further Issues in the Interpretation of Moderating Effects
Is the Moderating Effect Practically Significant?
The Signed Coefficient Rule for Interpreting Moderating Effects
The Importance on Identifying Criterion and Predictor A Priori
Conclusions
10. Summary and Conclusions
Moderators and Social Science Theory and Practice
Use of Moderated Multiple Regression
Homogeneity of Error Variance Assumption
Low Statistical Power and Proposed Remedies
Complex MMR Models
Assessing Practical Significance
Conclusions
Appendix A. Computation of Bartlett’s (1937) italMital
Statistic
Appendix B. Computation of James’s (1951) italJital
Statistic
Appendix C. Computation of Alexander’s (Alexander & Govern, 1994)
italAital Statistic
Appendix D. Computation of Modified
italfitalsuperscript2superscript
Appendix E. Theory-Based Power Approximation
References
Name Index
Subject Index
1. What Is a Moderator Variable and Why Should We Care?
Why Should We Study Moderator Variables?
Distinction between Moderator and Mediator Variables
Importance of A Priori Rationale in Investigating Moderating
Effects
Conclusions
2. Moderated Multiple Regression
What Is MMR?
Endorsement of MMR as an Appropriate Technique
Pervasive Use of MMR in the Social Sciences: Literature Review
Conclusions
3. Performing and Interpreting Moderated Multiple Regression
Analysis Using Computer Programs
Research Scenario
Data Set
Conducting an MMR Analysis Using Computer Programs: Two Steps
Output Interpretation
Conclusions
4. Homogeneity of Error Variance Assumption
What Is the Homogeneity of Error Variance Assumption?
Two Distinct Assumptions: Homoscedasticity and Homogeneity of Error
Variance
Is It a Big Deal to Violate the Assumption?
Violation of the Assumption in Published Research
How to Check If the Homogeneity Assumption Is Violated
What to Do When the Homogeneity of Error Variance Assumption Is
Violated
ALTMMR: Computer Program to Check Assumption Compliance and Compute
Alternative Statistics If Needed
Conclusions
5. MMR’s Low-Power Problem
Statistical Inferences and Power
Controversy Over Null Hypothesis Significance Testing
Factors Affecting the Power of All Inferential Tests
Factors Affecting the Power of MMR
Effect Sizes and Power in Published Research
Implications of Small Observed Effect Sizes for Social Science
Research
Conclusions
6. Light at the End of the Tunnel: How to Solve the Low-Power
Problem
How to Minimize the Impact of Factors Affecting the Power of All
Inferential Tests
How to Minimize the Impact of Factors Affecting the Power of
MMR
Conclusions
7. Computing Statistical Power
Usefulness of Computing Statistical Power
Empirically Based Programs
Theory-Based Program
Relative Impact of the Factors Affecting Power
Conclusions
8. Complex MMR Models
MMR Analyses Including a Moderator Variable with More Than Two
Levels
Linear Interactions and Non-linear Effects: Friends or Foes?
Testing and Interpreting Three-Way and Higher-Order Interaction
Effects
Conclusions
9. Further Issues in the Interpretation of Moderating Effects
Is the Moderating Effect Practically Significant?
The Signed Coefficient Rule for Interpreting Moderating Effects
The Importance on Identifying Criterion and Predictor A Priori
Conclusions
10. Summary and Conclusions
Moderators and Social Science Theory and Practice
Use of Moderated Multiple Regression
Homogeneity of Error Variance Assumption
Low Statistical Power and Proposed Remedies
Complex MMR Models
Assessing Practical Significance
Conclusions
Appendix A. Computation of Bartlett’s (1937) italMital
Statistic
Appendix B. Computation of James’s (1951) italJital
Statistic
Appendix C. Computation of Alexander’s (Alexander & Govern, 1994)
italAital Statistic
Appendix D. Computation of Modified
italfitalsuperscript2superscript
Appendix E. Theory-Based Power Approximation
References
Name Index
Subject Index
Herman Aguinis, PhD, is the Avram Tucker Distinguished Scholar and Professor of Management at George Washington University School of Business. Previously, he served on the faculties of the Kelley School of Business at Indiana University and the University of Colorado Denver Business School. In addition, he has been a visiting scholar at universities around the world. His research is interdisciplinary and addresses human capital acquisition, development, deployment, and research methods and analysis. Widely published, Dr. Aguinis currently serves as associate editor of the Annual Review of Organizational Psychology and Organizational Behavior. He is a Fellow of the Academy of Management, the American Psychological Association, the Association for Psychological Science, and the Society for Industrial and Organizational Psychology, and has been inducted into the Society of Organizational Behavior and the Society for Research Synthesis Methodology. His work has been recognized with numerous awards.
"'Aguinis has produced the most comprehensive single-source treatment on the topic of why and how to conduct moderated regression analysis for categorical moderators. The book presents very clear steps for how to test for moderators, but is more than a cookbook in that it also explores in detail the underlying assumptions; issues that will affect interpretation (e.g., homogeneity of variance and power); and solutions to frequently encountered problems. Examples from different types of research problems help clarify the analytical strategy, and presentation of the software for examining underlying issues is very valuable."
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