Comparing human vs. machine-assisted analysis to develop a new approach for Big Qualitative Data Analysis
Machine-assisted analysis offers substantial efficiency gains with acceptable accuracy trade-offs for large-scale qualitative data analysis. However, human expertise remains essential for capturing nuanced meanings, identifying emergent categories, and providing domain-specific interpretation. This hybrid approach represents a viable methodology for Big Qual research, though current AI tools' constraints in accommodating emergent classification schemes remain a limitation. Our findings establish benchmarks for future development of more flexible AI systems adapted to qualitative research paradigms.
Authors
- Noémie Deom (ORCID: https://orcid.org/0000-0001-9348-8970)
- E. Kursumovic (ORCID: https://orcid.org/0000-0003-0864-3564)
- Sophie Moniz (ORCID: https://orcid.org/0000-0002-1594-7323)
- Sam Martin (ORCID: https://orcid.org/0000-0002-4466-8374)
- Tim Cook
- Emma Beecham (ORCID: https://orcid.org/0000-0002-1832-0286)
- R. A. Armstrong (ORCID: https://orcid.org/0000-0001-9479-0143)
- Jasmeet Soar
- Cecilia Vindrola-Padros
- Collaborators
- Andrew D. Kane
Institutions
- North Bristol NHS Trust (GB)
- Royal College of Anaesthetists (GB)
- Royal United Hospital (GB)
- Southmead Hospital (GB)
- Oxford Research Group (GB)
- South Tees Hospitals NHS Foundation Trust (GB)
- University of Bristol (GB)
- University of Oxford (GB)
- Severn Trent (United Kingdom) (GB)
- Science Oxford (GB)
- Royal United Hospital Bath NHS Trust (GB)
- University College London (GB)
Publication Details
- Journal
- PLOS Digital Health
- Published
- 2026-02-25
- DOI
- https://doi.org/10.1371/journal.pdig.0000576
- Primary Topic
- Qualitative Research Methods and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00