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Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program

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  • Yang Yang

    (Texas Christian University)

  • Liran Ma

    (Miami University)

Abstract

Language-based text provide valuable insights into people’s lived experiences. While traditional qualitative analysis is used to capture these nuances, new paradigms are needed to scale qualitative research effectively. Artificial intelligence presents an unprecedented opportunity to expand the sale of analysis for obtaining such nuances. The study tests the application of GPT-4—a large language modeling—in qualitative data analysis using an existing set of text data derived from 60 qualitative interviews. Specifically, the study provides a practical guide for social and behavioral researchers, illustrating core elements and key processes, demonstrating its reliability by comparing GPT-generated codes with researchers’ codes, and evaluating its capacity for theory-driven qualitative analysis. The study followed a three-step approach: (1) prompt engineering, (2) reliability assessment by comparison of GPT-generated codes with researchers’ codes, and (3) evaluation of theory-driven thematic analysis on psychological constructs. The study underscores the utility of GPT’s capabilities in coding and analyzing text data with established qualitative methods while highlighting the need for qualitative expertise to guide GPT applications. Recommendations for further exploration are also discussed.

Suggested Citation

  • Yang Yang & Liran Ma, 2025. "Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(3), pages 2511-2534, June.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:3:d:10.1007_s11135-025-02066-1
    DOI: 10.1007/s11135-025-02066-1
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    References listed on IDEAS

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    1. Liao, Hongjing & Hitchcock, John, 2018. "Reported credibility techniques in higher education evaluation studies that use qualitative methods: A research synthesis," Evaluation and Program Planning, Elsevier, vol. 68(C), pages 157-165.
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    Cited by:

    1. Javier Orozco-Ospino & Gloria Florez-Yepes & Luis Diaz-Muegue, 2025. "Governance of Protected Areas Based on Effectiveness and Justice Criteria: A Qualitative Study with Artificial Intelligence-Assisted Coding," Sustainability, MDPI, vol. 17(19), pages 1-22, September.

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