Purdue University Graduate School
Overmyer Dissertation final July 29 20.pdf (703.53 kB)


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posted on 2021-10-12, 13:28 authored by Trinity C OvermyerTrinity C Overmyer

This dissertation details one of the first lines of inquiry into the rhetorical strategies used in scientific data analysis. The study primarily concerns the relationships between data work and knowledge making in the analysis of so-called “big data,” and how rhetoric and technical communication theories might inform those relationships. Hinging on five months embedded at a national science laboratory, this study uses ethnographic methods to detail the ways in which data analysis is neither purely data-driven and objective, nor purely situated in a local context or problem. Rather, data work requires both analytical processes and artful techne embedded in ongoing reflective praxis. As purely analytic, data work focuses on mathematical treatments, step by step procedures and rote formulas. As techne, data work requires interpretation. Rhetorical data analysis is not the opposite of data-driven work. Instead, rhetorical techne stands as the midpoint between the extremes of purely data-driven and purely context-driven analysis. Based on three cases that compare the practices of data novices, seasoned experts, and interdisciplinary teams, I argue that the ways in which scientists go about their data cleaning, collaboration, and analysis change based on their levels of expertise and the problem at hand. A number of principles that outline how data analysis is a form of rhetorical inscription are also defined, including the ways data dictionaries, model building and the construction of proxies intimately link scientific insights with language. The set of principles detailed in this dissertation are key areas that should be considered in both data science education and professional and technical writing curricula. Therefore, the project should be of particular interest to instructors and administrators in both Technical Writing and Data Science programs, as well as well as critical data studies scholars.


Degree Type

  • Doctor of Philosophy


  • English

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Patricia Sullivan

Additional Committee Member 2

Jennifer Bay

Additional Committee Member 3

Michael Salvo

Additional Committee Member 4

Benjamin Sims