Qualitative Coding in the Computational Era: A Hybrid Approach to Improve Reliability and Reduce Effort for Coding Ethnographic Interviews
AffiliationUniversity of Arizona, School of Sociology
Keywordscomputational social science
natural language processing
MetadataShow full item record
CitationZhuofan 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 2345
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AbstractSociologists 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.
NoteOpen access journal
VersionFinal published version
SponsorsUniversity of Arizona (Research, Discover, and Innovation Faculty Seed Grant) National Institute of Health (DP1AG069809, R01CA152195)
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/).