Chapter 7 shows how RIO can facilitate algorithmic case selection. We outline how algorithms can be used to select cases for in-depth analysis and provide two empirical analyses to illustrate how RIO facilitates a deeper understanding of how cases relate to one another within the model space, and how they align with the theoretical motivations for different case selection strategies.

Chapter 9 demonstrates how RIO facilitates a field-theoretic approach to regression models. The chapter draws parallels between the data representations made possible by turning regression models inside out and the geometric data analysis (GDA) that is central to field theoretic approaches to social research.

Chapter 2 introduces the logic, basic mathematics, and some of the benefits of turning regression inside out in the context of Ordinary Least Squares (OLS) regression. We do this through an in-depth reimagining of a classic analysis of the effects of welfare state spending on poverty. The chapter introduces novel techniques for regression decomposition, data visualization, and geometric data analysis.

Chapter 6 demonstrates one way that RIO can be used for exploratory data analysis: identifying statistically significant interaction terms. We show how exploring the relationships among cases offers important insights into the relationships between variables.

Chapter 1 introduces the general logic and motivations behind turning regression models “inside out.” Here, we explain how Regression Inside Out (RIO) facilitates a case-oriented approach to regression, detail the benefits of such an approach, and provide a roadmap for the rest of the book.

Chapter 3 demonstrates how the mathematics of turning Ordinary Least Squares (OLS) regression inside out can be generalized to Generalized Linear Models (GLM) including logistic, Poisson, negative binomial, random intercept, and fixed effects models.

Chapter 10 concludes our book, outlining the benefits of a case-oriented approach to regression. We review key substantive findings from the analyses presented in previous chapters and highlight directions for future research.

Chapter 5 shows how the methods introduced in the preceding chapters can be used to gain novel substantive and theoretical insights. We show how RIO can be used to identify multiple storylines implied by a single regression model by examining cases (or sets of cases) that contribute to the regression model in otherwise unseen ways. We illustrate RIO’s substantive benefits through empirical analyses of (1) the effects of regional integration on inequality, (2) the social determinants of health, and (3) the correlates of dog ownership.

Chapter 8 demonstrates how RIO allows analysts to incorporate the case-oriented logic of configurational comparative analysis into a regression framework. We contrast regression with qualitative comparative analysis (QCA), discuss key benefits of QCA, and outline a comparative configurational approach to regression. We illustrate how RIO can be used to realize some of the benefits of configurational comparative analysis through (1) a macro-comparative analysis of education-based inequality in political participation and (2) an analysis of the correlates of poverty using nationally representative data from the United States.