Regression Analysis in R: A Comprehensive View For The Social Sciences

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Acknowledgments
Chapter 1 Introduction
Contextualizing Correlation and Regression Analysis
Regression as Prediction
Regression as Explanation
Correlation, Regression, and Causation
Overview of This Book
Reference
Chapter 2 Correlation
Visualizing Relationships
Understanding Covariation
Simple Linear Relationships: The Pearson Product Moment Correlation Coefficient
Significance Testing for the Pearson r
Assumptions of the Pearson r
Alternative Correlations: Kendall Tau and Spearman Rho
The Spearman Rho
The Kendall Tau
Correlation Using R
Correlation Using {stats} Package
Correlation Using {Hmisc} Package
Matrix Scatterplots Using {Performance Analytics} Package
Chapter Summary
References
Chapter 2: End of Chapter Exercises
Chapter 3 Simple and Multiple Regression
Simple Linear Regression
Ordinary Least Squares (OLS) Regression
The Linear Regression Equation
Regression Model Fit
Multiple R
R2 and Adjusted R2
Standard Error of the Estimate
Multiple Regression Analysis
OLS Regression Using lm()
Summary
Chapter 3: End of Chapter Exercises
Chapter 4 Assumptions of Multiple Regression
Statistical Assumptions of Multiple Regression
Theoretical Assumptions or ‘Interpretational Considerations’
The Regression Model Is Theoretically Sound
Restriction of Range
Absence of Multicollinearity
Checking Assumptions of Multiple Regression Using R Software
Chapter 4: End of Chapter Exercises
Chapter 5 Dummy Variables and Interactions
Categorical Variables in Regression
Dummy Variables
A Note on the ‘0 0’ Category
Using/Interpreting Dummy Variables in a Regression Model
Interaction Effects in Regression Models
A Note on Including Main Effects and Centering for Products
Centering Predictors Using R
Chapter Summary
Chapter 5: End of Chapter Exercises
Chapter 6 Regression vs. ANOVA?
Analysis of Variance
ANOVA as Regression
ANOVA or Regression?
Chapter 7 Model Comparisons and Hierarchical Regression
Why Compare Models?
What Does It Mean for Models to Be Nested?
Model Comparisons for Nested and Non-Nested Models
Comparisons of Non-Nested Models
R Example of Non-Nested Model Comparison
Comparisons of Nested Models
Types of Nested Model Comparison
Chapter Summary
Chapter 7: End of Chapter Exercises
Chapter 8 Regression Extensions 1: Moderation/Mediation and Regression Discontinuity
Extension 1: Moderation
Extension 2: Regression Discontinuity
Motivating Example
Interpreting Treatment Effects in Regression Discontinuity Design
*A Note on the Terminology
Extension 3: Mediation
Baron and Kenny (1986) Requirements for Testing Mediation
Tests of Significance for the Indirect Effect
End of Chapter Summary
Recommended Resources
Chapter 8: End of Chapter Exercises
Chapter 9 Regression Extensions 2: Non-Linearity and Cross-Validation
Extension 4: Non-Linearity
Variable Transformations for Non-Linearity
Transformation Selection
What to Do with Negative Values?
Pros and Cons to the Transformation Approach
Use of Non-Linear Terms
Watch out for Multicollinearity!
Pros and Cons to the Use of Non-Linear Terms
Extension 5: Cross-Validation
Cross-Validation Samples
Cross-Validation Procedures
End of Chapter Summary
Chapter 9: End of Chapter Exercises
Chapter 10 Regression Extensions 3: Nested Data
Fixed Effects Modeling
Hierarchical Linear Modeling
Random Effects and the Tau Matrix
HLM Using R Software
Concluding Comments on Hierarchical Linear Modeling
Summary
Recommended Resources
Chapter 10: End of Chapter Exercises
Appendix A: Introduction to R
Appendix B: Non-Parametric Analysis Based on Ranks
Appendix C: R Function and Package Index
Appendix D: End of Chapter Exercise Script File Solutions
Appendix E: Glossary
Index