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Interaction Effects in Linear and Generalized Linear Models
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Interaction Effects in Linear and Generalized Linear Models
Examples and Applications Using Stata

October 2018 | 608 pages | SAGE Publications, Inc
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“This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results.”  

–Nicole Kalaf-Hughes, Bowling Green State University  

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata, and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression.  

The author’s website at www.icalcrlk.com provides a downloadable toolkit of Stata® routines to produce the calculations, tables, and graphics for each interpretive tool discussed. Also available are the Stata® dataset files to run the examples in the book.

Coming March 29 – 30th at Temple University – Robert L. Kaufman’s workshop on Using ICALC to Interpret Interaction Effects in Linear &
Generalized Linear Models. Learn more about registration, the workshop topics and schedule here.

 
Series Editor’s Introduction
 
Preface
 
Acknowledgments
 
About the Author
 
1. Introduction and Background
Overview: Why Should You Read This Book?

 
The Logic of Interaction Effects in Linear Regression Models

 
The Logic of Interaction Effects in GLMs

 
Diagnostic Testing and Consequences of Model Misspecification

 
Roadmap for the Rest of the Book

 
Chapter 1 Notes

 
 
PART I. PRINCIPLES
 
2. Basics of Interpreting the Focal Variable’s Effect in the Modeling Component
Mathematical (Geometric) Foundation for GFI

 
GFI Basics: Algebraic Regrouping, Point Estimates, and Sign Changes

 
Plotting Effects

 
Summary

 
Special Topics

 
Chapter 2 Notes

 
 
3. The Varying Significance of the Focal Variable’s Effect
Test Statistics and Significance Levels

 
JN Mathematically Derived Significance Region

 
Empirically Defined Significance Region

 
Confidence Bounds and Error Bar Plots

 
Summary and Recommendations

 
Chapter 3 Notes

 
 
4. Linear (Identity Link) Models: Using the Predicted Outcome for Interpretation
Options for Display and Reference Values

 
Reference Values for the Other Predictors (Z)

 
Constructing Tables of Predicted Outcome Values

 
Charts and Plots of the Expected Value of the Outcome

 
Conclusion

 
Special Topics

 
Chapter 4 Notes

 
 
5. Nonidentity Link Functions: Challenges of Interpreting Interactions in Nonlinear Models
Identifying the Issues

 
Mathematically Defining the Confounded Sources of Nonlinearity

 
Revisiting Options for Display and Reference Values

 
Solutions

 
Summary and Recommendations

 
Derivations and Calculations

 
Chapter 5 Notes

 
 
PART II. APPLICATIONS
 
6. ICALC Toolkit: Syntax, Options, and Examples
Overview

 
INTSPEC: Syntax and Options

 
GFI Tool: Syntax and Options

 
SIGREG Tool: Syntax and Options

 
EFFDISP Tool: Syntax and Options

 
OUTDISP Tool: Syntax and Options

 
Next Steps

 
Chapter 6 Notes

 
 
7. Linear Regression Model Applications
Overview

 
Single-Moderator Example

 
Two-Moderator Example

 
Special Topics

 
Chapter 7 Notes

 
 
8. Logistic Regression and Probit Applications
Overview

 
One-Moderator Example (Nominal by Nominal)

 
Three-Way Interaction Example (Interval by Interval by Nominal)

 
Special Topics

 
Chapter 8 Notes

 
 
9. Multinomial Logistic Regression Applications
Overview

 
One-Moderator Example (Interval by Interval)

 
Two-Moderator Example (Interval by Two Nominal)

 
Special Topics

 
Chapter 9 Notes

 
 
10. Ordinal Regression Models
Overview

 
One-Moderator Example (Interval by Nominal)

 
Two-Moderator Interaction Example (Nominal by Two Interval)

 
Special Topics

 
Chapter 10 Notes

 
 
11. Count Models
Overview

 
One-Moderator Example (Interval by Nominal)

 
Three-Way Interaction Example (Interval by Interval by Nominal)

 
Special Topics

 
Chapter 11 Notes

 
 
12. Extensions and Final Thoughts
Extensions

 
Final Thoughts: Dos, Don’ts, and Cautions

 
Chapter 12 Notes

 
 
Appendix: Data for Examples
Chapter 2: One-Moderator Example

 
Chapter 2: Two-Moderator Mixed Example

 
Chapter 2: Two-Moderator Interval Example

 
Chapter 2: Three-Way Interaction Example

 
Chapter 3: One-Moderator Example

 
Chapter 3: Two-Moderator Example

 
Chapter 3: Three-Way Interaction Example

 
Chapter 4: Tables One-Moderator Example and Figures Example 3

 
Chapter 4: Tables Two-Moderator Example

 
Chapter 4: Figures Examples 1 and 2

 
Chapter 4: Figures Example 4

 
Chapter 4: Tables Three-Way Interaction Example and Figures Example 5

 
Chapter 5: Examples 1 and 2

 
Chapter 5: Example 3

 
Chapter 5: Example 4

 
Chapter 6: One-Moderator Example

 
Chapter 6: Two-Moderator Example

 
Chapter 6: Three-Way Interaction Example

 
Chapter 7: One-Moderator Example

 
Chapter 7: Two-Moderator Example

 
Chapter 8: One-Moderator Example

 
Chapter 8: Three-Way Interaction Example

 
Chapter 9: One-Moderator Example

 
Chapter 9: Two-Moderator Example

 
Chapter 10: One-Moderator Example

 
Chapter 10: Two-Moderator Example

 
Chapter 11: One-Moderator Example

 
Chapter 11: Three-Way Interaction Example

 
Chapter 12: Polynomial Example

 
Chapter 12: Heckman Example

 
Chapter 12: Survival Analysis Example

 
 
References
Data Sources

 
 
Index

“This book is remarkable in its accessible treatment of interaction effects. Although this concept can be challenging for students (even those with some background in statistics), this book presents the material in a very accessible manner, with plenty of examples to help the reader understand how to interpret their results.”

Nicole Kalaf-Hughes
Bowling Green State University

“Interaction Effects in Linear and Generalized Linear Models provides an intuitive approach that benefits both new users of Stata getting acquainted with these statistical models as well as experienced students looking for a refresher. The topic of interactions is greatly important given that many of our main theories in the social and behavioral sciences rely on moderating effects of variables. This book does a terrific job of guiding the reader through the various statistical commands available in Stata and explaining the results and taking the reader through different considerations in graphically presenting their results.”

Jennifer Hayes Clark
University of Houston
Key features

KEY FEATURES:

  • Detailed discussions show students how to apply and interpret results for linear, multinomial logistic regression, ordinal regression models, and poison and negative binomial regression models (including zero-inflated variants). 
  • Downloadable software to apply interpretive tools automates the calculation and creation of numeric results, tables and graphics, reducing the need for more complex programming skills.
  • Sufficient mathematical detail and application of underlying formulas, as well as discussion of specific applications, enables readers to develop their own spreadsheet formulas or software applications.
  • The ICALC toolkit saves all graphics as memory graphs which can be customized during a Stata session or saved/exported for later customization and use and optionally saves numeric results, tables and the underlying data for graphics into an Excel spreadsheet to provide flexibility for users to create their own graphics from the data using other platforms.

 

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