Interaction Effects in Linear and Generalized Linear Models

Examples and Applications Using Stata
Robert L. Kaufman
Advanced Quantitative Techniques in the Social Sciences
Interaction Effects in Linear and Generalized Linear Models
September 2018 | 608 pages | Sage US
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Description

“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.



Contents

Series Editor’s Introduction

Series Editor’s Introduction

Preface

Preface

Acknowledgments

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

Index

Description

“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.



Contents

Series Editor’s Introduction

Series Editor’s Introduction

Preface

Preface

Acknowledgments

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

Index

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Interaction Effects in Linear and Generalized Linear Models

Examples and Applications Using Stata

Robert L. Kaufman
Cancel Print

September 2018 | 608 pages | Sage US

Format Published Date ISBN Price

“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.




Table Of Contents:

  • 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

Recent Product Reviews:

“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

Recommendations