Formally comparing topic models and human-generated qualitative coding of physician mothers' experiences of workplace discrimination

Bibliographic Data

ID5261071
AuthorsAdam S Miner (0000-0002-5125-4735, Stanford University), Samuel Alexander Stewart (0000-0001-5789-8390, Stanford University, corresponding author), Meghan C Halley (0000-0002-5031-9840, Ethics and Public Policy Center), Livingston Krumah Nelson (0000-0001-8948-300X, University of British Columbia), Eleni Linos (0000-0002-5856-6301, Stanford University)
Year2023
Volume10
Issue1
Publication date2023-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBig Data & Society (JOURNAL)
Journal identifiersISSN: 2053-9517 • E-ISSN: 2053-9517
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20539517221149106
OpenAlexW4317910427
LanguageEN
Citations received15
References cited47

Differences between computationally generated and human-generated themes in unstructured text are important to understand yet difficult to assess formally. In this study, we bridge these approaches through two contributions. First, we formally compare a primarily computational approach, topic modeling, to a primarily human-driven approach, qualitative thematic coding, in an impactful context: physician mothers' experience of workplace discrimination. Second, we compare our chosen topic model to a principled alternative topic model to make explicit study design decisions meriting consideration in future research. By formally contrasting computationally generated (i.e. topic modeling) and human-generated (i.e. thematic coding) knowledge, we shed light on issues of interest to several audiences, notably computational social scientists who wish to understand study design tradeoffs, and qualitative researchers who may wish to leverage computational methods to improve the speed and reproducibility of labor-intensive coding. Although useful in other domains, we highlight the value of fast, reproducible methods to better understand experiences of workplace discrimination

Data science · Qualitative research · Sociology · Thematic analysis · Topic model · Computational and Text Analysis Methods · Computer Science · Qualitative Research Methods and Ethics · Social Science and Policy Research · Artificial Intelligence

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Open DOIOpen Access