Interaction Effects in Linear and Generalized Linear Models

Robert L. Kaufman - Temple University, USA

Series:
Advanced Quantitative Techniques in the Social Sciences

October 2018 | 608 pages | SAGE Publications, Inc

“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.

Available Formats

ISBN: 9781506365374	Hardcover	Suggested Retail Price: $169.00	Bookstore Price: $135.20
ISBN: 9781506365381	Electronic Version	Suggested Retail Price: $97.00	Bookstore Price: $97.00

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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

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.