About

David Melamed is a Professor in the Department of Sociology at The Ohio State University. His areas of expertise include groups, processes, networks, and methodology. His research focuses on how social structures such as network structures, status structures, and class structures influence individual outcomes. Specific topics of interest include how network structures shape prosocial behaviors, how status structures impact small group inequalities, and how macro-level class structures affect class and occupational outcomes. Some of his work has been supported by the Army Research Office and the National Science Foundation. For more information, his personal website is available at u.osu.edu/melamed9.

Research topics

• Computer Science
• Sociology
• Psychology
• Social psychology
• Artificial Intelligence
• Economics
• Computer Security
• Social Science
• Economic geography
• Business
• Economic system
• Microeconomics
• Biology
• Cognitive psychology
• Communication
• World Wide Web
• Mathematics
• Marketing

Selected publications

• Inequality in Face-to-Face Interactions: Status, Prototypicality, and Subgrouping

  Small Group Research · 2026-01-20

  articleSenior author

  We report on the first experimental tests of the integration of status characteristics theory and self-categorization theory. Using 3-person and 6-person group interaction studies, we find mixed support. While members who have higher status in the group, or who have traits prototypical of the group, do impart greater influence over group decision-making, for members who have both high status and prototypical characteristics, the benefits are not additive. Group members are also no more likely to direct behaviors to a fellow group member who displays prototypical traits than to either non-prototypical fellow group members or individuals in an outgroup.

  PublisherDOI

• Embedding of X-Ray Computed Tomography Data of Cultural Heritage Objects in Interactive Web Applications – Old Technical Instruments Brought Back to Novel Virtual Life

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• RIO as a Gateway to Field Theory

  Cambridge University Press eBooks · 2024-02-22

  book-chapter

  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.

  PublisherDOI

• Regression Inside Out

  Cambridge University Press eBooks · 2024-02-22 · 3 citations

  book

  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.

  PublisherDOI

• Action and Interaction

  Cambridge University Press eBooks · 2024-02-22

  book-chapter

  A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

  PublisherDOI

• Turning Regression Inside Out

  Cambridge University Press eBooks · 2024-02-22

  book-chapter

  A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

  PublisherDOI

• Measuring Gender Status Beliefs

  Socius Sociological Research for a Dynamic World · 2024-01-01 · 3 citations

  articleOpen accessSenior author

  The implicit association test (IAT) is designed to reduce socially desirable responses and capture implicit associations between two social categories. Prior work has used and expanded on the IAT to capture implicit status beliefs, but tests of the specific images and words used to denote status and gender are lacking. Here, the authors (1) identify specific images to best elicit implicit stereotypical gender differentiation, (2) identify specific words to best distinguish relative status, and (3) assess the test-retest reliability of a full and a brief gender status IAT. First, the authors find that images presented in grayscale, rather than images presented in color, best elicit implicit gender categorization. The authors also identify five male and five female images that best elicit implicit stereotypical gender categorization. Second, the findings show that status words and evaluation words load on unique factors (highlighting that the status words are not merely capturing evaluations), and the authors identify five specific words that best distinguish implicit relative status. Third, the authors find that the standard long-form IAT has a more acceptable test-retest reliability than the brief IAT. The authors conclude with suggestions on how to further refine the measure and how it might be applied in research.

  PublisherOA PDFDOI

• Conclusion

  Cambridge University Press eBooks · 2024-02-22

  book-chapter

  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.

  PublisherDOI

• catregs: Post-Estimation Functions for Generalized Linear Mixed Models

  2024-06-11 · 1 citations

  datasetOpen access1st authorCorresponding

  Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

  PublisherOA PDFDOI

• RIO as a Gateway to Case Selection

  Cambridge University Press eBooks · 2024-02-22

  book-chapter

  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.

  PublisherDOI

Recent grants

• Collaborative Research: The Embeddedness of Indirect and Generalized Reciprocity

  NSF · $96k · 2016–2019

• Status, Faction Sizes and Social Influence

  NSF · $96k · 2015–2017

• SBP: Theories of Social Status and Inequality

  NSF · $181k · 2019–2022

Frequent coauthors

• Brent Simpson

  University of South Carolina

  19 shared

• Ronald L. Breiger

  15 shared

• Ashley Harrell

  Duke University

  14 shared

• Eric W. Schoon

  14 shared

• Jered Abernathy

  University of South Carolina

  11 shared

• Matthew Sweitzer

  Sandia National Laboratories

  10 shared

• Christopher Munn

  9 shared

• Scott Savage

  9 shared