Qualitative Coding in the Computational Era: A Hybrid Approach to Improve Reliability and Reduce Effort for Coding Ethnographic Interviews
Affiliation
University of Arizona, School of SociologyIssue Date
2021-12-06Keywords
computational social sciencemachine learning
natural language processing
coding reliability
computational ethnography
qualitative methods
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SAGE PublicationsCitation
Zhuofan Li, Daniel Dohan, and Corey M Abramson. 2021. “Qualitative Coding in the Computational Era: A Hybrid Approach to Improve Reliability and Reduce Effort for Coding Ethnographic Interviews.” Socius 7. https://doi.org/10.1177/2378023121106 2345Journal
SociusRights
© The Author(s) 2021. This article is distributed under the terms of the Creative Commons Attribution NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Sociologists have argued that there is value in incorporating computational tools into qualitative research, including using machine learning to code qualitative data. Yet standard computational approaches do not neatly align with traditional qualitative practices. The authors introduce a hybrid human-machine learning approach (HHMLA) that combines a contemporary iterative approach to qualitative coding with advanced word embedding models that allow contextual interpretation beyond what can be reliably accomplished with conventional computational approaches. The results, drawn from an analysis of 87 human-coded ethnographic interview transcripts, demonstrate that HHMLA can code data sets at a fraction of the effort of human-only strategies, saving hundreds of hours labor in even modestly sized qualitative studies, while improving coding reliability. The authors conclude that HHMLA may provide a promising model for coding data sets where human-only coding would be logistically prohibitive but conventional computational approaches would be inadequate given qualitative foci.Note
Open access journalISSN
2378-0231EISSN
2378-0231Version
Final published versionSponsors
University of Arizona (Research, Discover, and Innovation Faculty Seed Grant) National Institute of Health (DP1AG069809, R01CA152195)ae974a485f413a2113503eed53cd6c53
10.1177/23780231211062345
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Except where otherwise noted, this item's license is described as © The Author(s) 2021. This article is distributed under the terms of the Creative Commons Attribution NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).