Thematic analysis of interview data with ChatGPT: designing and testing a reliable research protocol for qualitative research

Manuel Goyanes (), Carlos Lopezosa and Beatriz Jordá
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Manuel Goyanes: Universidad Carlos III de Madrid
Carlos Lopezosa: Universitat de Barcelona
Beatriz Jordá: Universidad Carlos III de Madrid

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 6, No 25, 5510 pages

Abstract: Abstract In recent years, artificial intelligence has developed into a powerful tool for processing and generating human-like texts, unlocking innovative possibilities for quantitative and qualitative data analysis. Within qualitative research, artificial intelligence in general, and ChatGPT in particular, represent promising avenues to explore and examine textual transcriptions from interview data. This study is a step forward in this direction, advancing a reliable research protocol for using ChatGPT to conduct thematic analysis, which includes the following standard steps: (1) data preparation, (2) defining the analysis process, (3) chatbot interaction, (4) iterative process, (5) review and validation, and (6) analysis and interpretation. Results of the analysis revealed that ChatGPT may significantly facilitate qualitative data analysis exploration, especially during initial research stages and when dealing with extensive transcription material. Additionally, our protocol design is able to reliably identify different thematic patterns emerging from the text, although the granularity of the output may vary depending on the quality of the prompt and human intelligence interpretation. Accordingly, we conclude that despite its vast power, the ChatGPT model in its current state is unable to substitute the contextual insights and subtle metaphorical nuances associated with human qualitative analysis, interpretation and reflexivity.

Keywords: Thematic analysis; ChatGPT; Qualitative research; Qualitative data analysis; Interview data; Transcriptions (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02199-3

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