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 (downloadable from the Robert L. Kaufman’s website), 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 data sets and the Stata code to reproduce the results of the application examples are available online.
|Series:||Advanced Quantitative Techniques in the Social Sciences Series , #12|
|Product dimensions:||7.00(w) x 10.00(h) x (d)|
About the Author
Robert Kaufman (Ph D 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.
Table of Contents
Series Editor’s IntroductionPrefaceAcknowledgmentsAbout the Author1. 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 NotesPART I. PRINCIPLES2. 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 Notes3. 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 Notes4. 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 Notes5. 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 NotesPART II. APPLICATIONS6. 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 Notes7. Linear Regression Model Applications Overview Single-Moderator Example Two-Moderator Example Special Topics Chapter 7 Notes8. 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 Notes9. Multinomial Logistic Regression Applications Overview One-Moderator Example (Interval by Interval) Two-Moderator Example (Interval by Two Nominal) Special Topics Chapter 9 Notes10. Ordinal Regression Models Overview One-Moderator Example (Interval by Nominal) Two-Moderator Interaction Example (Nominal by Two Interval) Special Topics Chapter 10 Notes11. Count Models Overview One-Moderator Example (Interval by Nominal) Three-Way Interaction Example (Interval by Interval by Nominal) Special Topics Chapter 11 Notes12. Extensions and Final Thoughts Extensions Final Thoughts: Dos, Don’ts, and Cautions Chapter 12 NotesAppendix: 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 ExampleReferences Data SourcesIndex