David Melamed is a Professor of Sociology and Translational Data Analytics at The Ohio State University. His research interests include the emergence of stratification, cooperation and segregation in dynamical systems, and statistics and methodology. Since 2019 he has been co-Editor of Sociological Methodology. Long Doan is an Associate Professor of Sociology at the University of Maryland, College Park. His research examines how various social psychological processes like identity, intergroup competition, and bias help to explain the emergence and persistence of social stratification. He focuses on inequalities based on sexuality, gender, and race.

• 1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

"Overall, Applications of Regression for Categorical Outcomes Using R is a well-written and valuable introduction to modeling categorical outcomes for graduate students and practitioners in the social sciences and related disciplines. The book achieves its aims (described using a mountain climbing analogy) to “explain how to choose which peak totackle given an empirical problem, distinguish among truly different options, and equivocate when choices are more preferences than substantive decisions.” (pg. 1) The consistent structure across chapters, focus on conceptual understanding, and guidance on grappling with practical considerations make it a nice text for an applied graduate course or for those seeking to learn (or review) these methods."- Maria Tackett, “Applications of Regression for Categorical Outcomes Using R.” The American Statistician, August 2025.

David Melamed is a Professor of Sociology and Translational Data Analytics at The Ohio State University. His research interests include the emergence of stratification, cooperation and segregation in dynamical systems, and statistics and methodology. Since 2019 he has been co-Editor of Sociological Methodology. Long Doan is an Associate Professor of Sociology at the University of Maryland, College Park. His research examines how various social psychological processes like identity, intergroup competition, and bias help to explain the emergence and persistence of social stratification. He focuses on inequalities based on sexuality, gender, and race.

• 1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

"Overall, Applications of Regression for Categorical Outcomes Using R is a well-written and valuable introduction to modeling categorical outcomes for graduate students and practitioners in the social sciences and related disciplines. The book achieves its aims (described using a mountain climbing analogy) to “explain how to choose which peak totackle given an empirical problem, distinguish among truly different options, and equivocate when choices are more preferences than substantive decisions.” (pg. 1) The consistent structure across chapters, focus on conceptual understanding, and guidance on grappling with practical considerations make it a nice text for an applied graduate course or for those seeking to learn (or review) these methods."- Maria Tackett, “Applications of Regression for Categorical Outcomes Using R.” The American Statistician, August 2025.

David Melamed is a Professor of Sociology and Translational Data Analytics at The Ohio State University. His research interests include the emergence of stratification, cooperation and segregation in dynamical systems, and statistics and methodology. Since 2019 he has been co-Editor of Sociological Methodology. Long Doan is an Associate Professor of Sociology at the University of Maryland, College Park. His research examines how various social psychological processes like identity, intergroup competition, and bias help to explain the emergence and persistence of social stratification. He focuses on inequalities based on sexuality, gender, and race.

• 1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data

"Overall, Applications of Regression for Categorical Outcomes Using R is a well-written and valuable introduction to modeling categorical outcomes for graduate students and practitioners in the social sciences and related disciplines. The book achieves its aims (described using a mountain climbing analogy) to “explain how to choose which peak totackle given an empirical problem, distinguish among truly different options, and equivocate when choices are more preferences than substantive decisions.” (pg. 1) The consistent structure across chapters, focus on conceptual understanding, and guidance on grappling with practical considerations make it a nice text for an applied graduate course or for those seeking to learn (or review) these methods."- Maria Tackett, “Applications of Regression for Categorical Outcomes Using R.” The American Statistician, August 2025.