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28 results

Page 1 of 2

Regression Inside Out
  • Eric W. Schoon, David Melamed, Ronald L. Breiger
  • Published online:
    21 March 2024
    Print publication:
    22 February 2024
  • Linear regression analysis, with its many generalizations, is the predominant quantitative method used throughout the social sciences and beyond. The goal of the method is to study relations among variables. In this book, Schoon, Melamed and Breiger turn regression modeling inside out to put the emphasis on the cases (people, organizations, and nations) that comprise the variables. By re-analyzing influential published research, they reveal new insights and present a principled way to unlock a set of more nuanced interpretations than has previously been attainable. The emphasis is on intuition and examples that can be reproduced using the code and datasets provided. Relating their contributions to methodologies that operate under quite different philosophical assumptions, the authors advance multi-method social science and help to bridge the divide between quantitative and qualitative research. The result is a modern, accessible, and innovative take on extracting knowledge from data.

  • 7 - RIO as a Gateway to Case Selection

  • from Part III - RIO as a Gateway
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 127-171

  • Summary

    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.

  • 9 - RIO as a Gateway to Field Theory

  • from Part III - RIO as a Gateway
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 202-219

  • Summary

    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.

  • 2 - OLS Inside Out

  • from Part I - Turning Regression Inside Out
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 17-49

  • Summary

    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.

  • 6 - Interaction Detection

  • from Part II - Action and Interaction
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 113-124

  • Summary

    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.

  • 1 - Introduction

  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 1-14

  • Summary

    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.

  • 10 - Conclusion

  • from Part III - RIO as a Gateway
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 220-226

  • Summary

    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.

  • 5 - Action Detection

  • from Part II - Action and Interaction
  • Eric W. Schoon, Ohio State University, David Melamed, Ohio State University, Ronald L. Breiger, University of Arizona
  • Book:
    Regression Inside Out
    Published online:
    21 March 2024
    Print publication:
    22 February 2024, pp 81-112

  • Summary

    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.

Page 1 of 2