Series Editor Introduction
Preface
Acknowledgements
About the Authors
Chapter 1: Introduction
What Are Dummy Variables?
Review of Multiple Regression and Functional Forms
Using OLS to Analyze Personal Income
Chapter 2: Creating and Using Binary-Coded Dummy Variables
Correlation with Dummy Variables
Chapter 3: Using Dummy Variables as Regressors
Regression With One Dummy Variable
Regression With Two Dummy Variables
Regression With Multiple Dummy Variables for a Single Classification
Assessing Income Differences Between Included Categories
Including Three Classifications Using Seven Dummy Variables
Shifting Reference Groups and Zero-Points
Chapter 4: Assessing Whether Relationships Differ by Group
Revisiting Some Assumptions
Interaction with Two Dichotomous Relationships
Interacting Binary and Multicategory Classifications
Adding a Quantitative Independent Variable
Illustrating these Relationships
Interaction Between Multicategory and Quantitative Variables
Illustrating Interaction Effects
Interaction of Dichotomous with Quadratic Variables
Separate Subgroup Regressions
Chapter 5: Specification, Significance, and Assumptions
Recoding Discrete Independent Variables
Specifying Education Using Categories
Specifying Education Using a Quadratic Function
Specifying Education Using Piecewise Linear Regression
Interpreting Dummy Variables in Semilogarithmic Equations
Interpreting Dummy Variables with Square Root Transformations
Interpreting Dummy Variables in Logit Models
Dealing with Group Heteroscedasticity
Making Multiple Comparisons with Non-independent Tests
Chapter 6: Alternative Coding Schemes for Dummy Variables
Effects-Coded Dummy Variables
Contrast-Coded Dummy Variables
Conclusion
Notes
References
