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ABOUT THE BOOK

Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers through bivariate correlation giving them a solid framework from which to expand into more complicated regression models. Concepts are demonstrated using R software and real data examples.

Key Features:

• Full output examples complete with interpretation
• Full syntax examples to help teach R code
• Appendix explaining basic R functions
• Methods for multilevel data that are often included in basic regression texts
• End of Chapter Comprehension Exercises

TABLE OF CONTENTS

Chapter 1.  Introduction, Relationships and the Issue of Causality

 Chapter 2.  Describing Simple Relationship

 2.1  Pearson Correlations

  2.1.1Computation

  2.1.2 R Examples

  2.2  Extensions of the Pearson Correlation

   2.2.1 Point Bi-Serial Correlation

       2.2.2 Phi Coefficient

       2.2.3 Spearman Rho

            End of Chapter Comprehension Exercises

 Chapter 3.  Linear Regression Analysis

3.1       Simple Linear Regression

3.1.1    Equations

3.1.2    Model Fit Statistics

3.1.3    Significance Tests

3.2       Multiple Linear Regression

3.3       R Examples

End of Chapter Comprehension Exercises

Chapter 4.  Regression Assumptions and Interpretational Considerations

            4.1 Statistical Assumptions

            4.2 Theoretical Assumptions

            4.3 Interpretational Considerations

                        4.3.1Multicollinearity

                        4.3.2 Restriction of Range

                        4.3.3 Variability

End of Chapter Comprehension Exercises

Chapter 5.  Dummy Variables and Interactions

            5.1 Dummy coding

                        5.1.1 Dummy codes for 3 or more levels

                        5.1.2 Interpretation Examples

            5.2 Interactions

                        5.2.1 Creating Interactions

                        5.2.2 Mean Centering for Interactions

                        5.2.3 Interpretation Examples

            End of Chapter Comprehension Exercises

Chapter 6.  Hierarchical Regression

            6.1 Types of Hierarchical Regression

            6.2 Model Comparison Statistics

            6.3 R Examples

            End of Chapter Comprehension Exercises

Chapter 7.  Moderation and Mediation

            7.1 Moderation

            7.2 Mediation

                        7.2.1 Baron and Kenny

                        7.2.2 Tests of Indirect effects

            End of Chapter Comprehension Exercises

Chapter 8.  Dealing with Non Linearity

            8.1 Transformations

            8.2 Non Linear Terms

            8.3 Overfitting – cross validation

            End of Chapter Comprehension Exercises

Chapter 9. Regression Models for Nested Data

            9.1 Fixed Effects Modeling

            9.2 Hierarchical Linear Modeling

            End of Chapter Comprehension Exercises

Appendix A

            Basic R Use

Appendix B

            Exercise Answers