Elementary Regression Modeling

Roger A. Wojtkiewicz is a professor in the Department of Sociology at Ball State University in Muncie, Indiana. He spent the first 12 years of his career in the Department of Sociology at Louisiana State University and has since been at Ball State where he served as department chairperson for 12 years. At LSU, he taught undergraduate statistics and a graduate course in regression modeling in the PhD program. At Ball State, he has taught both the first and second semester courses in the statistics sequence in the master's program. He was trained as a quantitative methodologist in the graduate sociology program at the University of Wisconsin-Madison. He is author of Elementary Regression Modeling: A Discrete Approach published by Sage Publications in 2017.

Chapter 1: Introductory Ideas Regression Modeling Control Modeling Modeling Interactions Modeling Linearity With Splines Testing Research Hypotheses Classical Approach to Regression Disadvantages of Classical Approach Discrete Approach to Regression Summary Key Concepts Notes Chapter 2: Basic Statistical Procedures Individual Units and Groups Measurement Level of Measurement Examples for Level of Measurement Count, Sum, and Transformations Mean Proportion and Percentage Odds and Log odds Examples of Means and Log Odds Differences Summary Key Concepts Chapter Exercises Notes Chapter 3: Regression Modeling Basics Difference between Means: The t-test Linear Regression With a Two-Category Independent Variable Logistic Regression With a Two-Category Independent Variable Linear Regression With a Four-Category Independent Variable Logistic Regression With a Four-Category Independent Variable Modeling Linear Effect With Dummy Variables Linear Coefficient in Linear Regression Linear Coefficient in Logistic Regression Using Dummy Variables for a Continuous Variable Summary Key Concepts Chapter Exercises Notes Chapter 4: Key Regression Modeling Concepts Unit Vector: Estimating the Intercept Nestedness Higher-Order Differences Constraints Summary Key Concepts Chapter Exercises Notes Chapter 5: Control Modeling Elementary Control Modeling Elaboration for Controlling Demographic Standardization for Controlling Small and Big Models Allocating Influence With Multiple Control Variables One-at-a-Time Without Controls Step Approach One-at-a-Time With Controls Hybrid Approach Nestedness and Constraints Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 6: Modeling Interactions Interactions as Conditional Differences Interactions Between Dummy Variables Interactions Between Dummy Variables and an Interval Variable Three-Way Interactions Estimating Separate Models Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 7: Modeling Linearity With Splines Dummy Variables Nested in an Interval Variable Introduction to Knotted Spline Variables Spline Variables Nested in an Interval Variable Regression Modeling Using Spline Variables Working With a Continuous Independent Variable Example Using Logistic Regression Summary Key Concepts Chapter Exercises Notes Chapter 8: Conclusion: Testing Research Hypotheses Bivariate Hypothesis/No Controls Bivariate Hypothesis/Unanalyzed Controls Bivariate Hypothesis/Analyzed Controls Hypothesis Involving Interactions Hypothesis Involving Nonlinearity Final Comments Key Concepts Summary Chapter exercises Notes