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Methodological Transparency and Big Data: A Critical Comparative Analysis of Institutionalization

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Information in Contemporary Society (iConference 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11420))

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Abstract

Big data is increasingly employed in predictive social analyses, yet there are many visible instances of unreliable models or failure, raising questions about methodological validity in data driven approaches. From meta-analysis of methodological institutionalization across three scholarly disciplines, there is evidence that traditional statistical quantitative methods, which are more institutionalized and consistent, are important to develop, structure, and institutionalize data scientific approaches for new and large n quantitative methods, indicating that data driven research approaches may be limited in reliability, validity, generalizability, and interpretability. Results also indicate that interdisciplinary collaborations describe methods in significantly greater detail on projects employing big data, with the effect that institutionalization makes data science approaches more transparent.

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Author information

Authors and Affiliations

  1. Center for Information Technology, Princeton University, Princeton, NJ, 08544, USA

    Madelyn Rose Sanfilippo

  2. Department of Information and Library Science, Indiana University, Bloomington, IN, 47408, USA

    Chase McCoy

Authors
  1. Madelyn Rose Sanfilippo
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  2. Chase McCoy
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Correspondence to Madelyn Rose Sanfilippo .

Editor information

Editors and Affiliations

  1. University of South Florida, Tampa, FL, USA

    Natalie Greene Taylor

  2. University of Maryland, College Park, MD, USA

    Caitlin Christian-Lamb

  3. University of Washington, Seattle, WA, USA

    Michelle H. Martin

  4. University of California, Irvine, Irvine, CA, USA

    Bonnie Nardi

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Sanfilippo, M.R., McCoy, C. (2019). Methodological Transparency and Big Data: A Critical Comparative Analysis of Institutionalization. In: Taylor, N., Christian-Lamb, C., Martin, M., Nardi, B. (eds) Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science(), vol 11420. Springer, Cham. https://doi.org/10.1007/978-3-030-15742-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-15742-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15741-8

  • Online ISBN: 978-3-030-15742-5

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