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