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The Extended Computational Case Method: A Framework for Research Design

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  • Juan Pablo Pardo-Guerra
  • Prithviraj Pahwa

Abstract

This paper considers the adoption of computational techniques within research designs modeled after the extended case method. Echoing calls to augment the power of contemporary researchers through the adoption of computational text analysis methods, we offer a framework for thinking about how such techniques can be integrated into quasi-ethnographic workflows to address broad, structural sociological claims. We focus, in particular, on how this adoption of novel forms of evidence impacts corpus design and interpretation (which we tie to matters of casing), theoretical elaboration (which we associate to moving empirical claims across scales and empirical domains), and verification (which we see as a process of reflexive scaffolding of theoretical claims). We provide an example of the use of this framework through a study of the marketization of social scientific knowledge in the United Kingdom.

Suggested Citation

  • Juan Pablo Pardo-Guerra & Prithviraj Pahwa, 2022. "The Extended Computational Case Method: A Framework for Research Design," Sociological Methods & Research, , vol. 51(4), pages 1826-1867, November.
    • Handle: RePEc:sae:somere:v:51:y:2022:i:4:p:1826-1867
    DOI: 10.1177/00491241221122616
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    References listed on IDEAS

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    1. Björn Hammarfelt & Sarah de Rijcke, 2015. "Accountability in context: effects of research evaluation systems on publication practices, disciplinary norms, and individual working routines in the faculty of Arts at Uppsala University," Research Evaluation, Oxford University Press, vol. 24(1), pages 63-77.
    2. Sarah Rijcke & Alexander Rushforth, 2015. "To intervene or not to intervene; is that the question? On the role of scientometrics in research evaluation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1954-1958, September.
    3. Taylor, Marshall A. & Stoltz, Dustin S., 2020. "Integrating Semantic Directions with Concept Mover's Distance to Measure Binary Concept Engagement," SocArXiv 36r2d, Center for Open Science.
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    Cited by:

    1. Yung, Vincent & Colyvas, Jeannette, 2023. "Munging the Ghosts in the Machine: Coded Bias and the Craft of Wrangling Archival Data," SocArXiv 2dve6, Center for Open Science.
    2. Yung, Vincent & Colyvas, Jeannette & Hwang, Hokyu, 2023. "Quality Control for Quality Computational Concepts: Wrangling with Theory and Data Wrangling as Theorizing," SocArXiv ewamx, Center for Open Science.

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