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The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods

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  • Laura K. Nelson
  • Derek Burk
  • Marcel Knudsen
  • Leslie McCall

Abstract

Advances in computer science and computational linguistics have yielded new, and faster, computational approaches to structuring and analyzing textual data. These approaches perform well on tasks like information extraction, but their ability to identify complex, socially constructed, and unsettled theoretical concepts—a central goal of sociological content analysis—has not been tested. To fill this gap, we compare the results produced by three common computer-assisted approaches—dictionary, supervised machine learning (SML), and unsupervised machine learning—to those produced through a rigorous hand-coding analysis of inequality in the news ( N = 1,253 articles). Although we find that SML methods perform best in replicating hand-coded results, we document and clarify the strengths and weaknesses of each approach, including how they can complement one another. We argue that content analysts in the social sciences would do well to keep all these approaches in their toolkit, deploying them purposefully according to the task at hand.

Suggested Citation

  • Laura K. Nelson & Derek Burk & Marcel Knudsen & Leslie McCall, 2021. "The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods," Sociological Methods & Research, , vol. 50(1), pages 202-237, February.
    • Handle: RePEc:sae:somere:v:50:y:2021:i:1:p:202-237
    DOI: 10.1177/0049124118769114
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    References listed on IDEAS

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    Cited by:

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    4. Daniel Caballero-Julia & Philippe Campillo, 2021. "Epistemological Considerations of Text Mining: Implications for Systematic Literature Review," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    5. AJ Alvero & Jasmine Pal & Katelyn M. Moussavian, 2022. "Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays," Journal of Computational Social Science, Springer, vol. 5(2), pages 1709-1734, November.
    6. Konstantin Gavras & Jan Karem Höhne & Annelies G. Blom & Harald Schoen, 2022. "Innovating the collection of open‐ended answers: The linguistic and content characteristics of written and oral answers to political attitude questions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 872-890, July.
    7. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.

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