Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata(Advanced Quantitative Techniques in the Social Sciences)

"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 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.

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

About the Author :
Robert Kaufman (PhD University of Wisconsin, 1981) is professor of sociology and the Chair of the Department of Sociology at Temple University. His substantive research focuses on economic structure and labor market inequality, especially with respect to race, ethnicity, and gender. He has also explored other realms of race-ethnic inequality, including research on wealth, home equity, residential segregation, traffic stops and treatment by police, and media portrayals of crime. More abstract statistical issues motivate some of his current work on evaluating different methods for correcting for heteroskedasticity using Monte Carlo simulations. Dr. Kaufman has published papers on quantitative methods in American Sociological Review, American Journal of Sociology, Sociological Methodology, Sociological Methods and Research, and Social Science Quarterly. He served on the editorial board of Sociological Methods and Research for 15 years and has taught graduate-level statistics courses nearly every year for the past 30 years.

Review :
"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." "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."