Qualitative Content Analysis With ChatGPT: Pitfalls, Rough Approximations and Gross Errors. A Field Report
DOI:
https://doi.org/10.17169/fqs-26.1.4252Keywords:
ChatGPT, artificial intelligence, qualitative content analysis, theme analysis, qualitative text analysis, grounded-theory-methodology, psychoanalytical text interpretationAbstract
In this article I describe a series of test runs to examine the contribution that the AI-based program ChatGPT in both Versions 3.5. and 4 can make to a qualitative content analysis of interview texts. A short sample text with my sample solution is presented for this purpose. Rough inputs for a rather naive use ("Conduct a qualitative content analysis!") as well as differentiated specifications with questions and more precise coding instructions (prompts) led in both versions at most to rough approximations of the sample solution with a large number of gross errors. The program did not react or reacted incorrectly to different content analysis concepts (BRAUN & CLARKE, 2006; KUCKARTZ, 2014; MAYRING, 2022a; SCHREIER, 2012), did not recognize hidden text content, and failed to check for coding agreement. The results of the software, no matter what specifications were made, mostly pointed in the direction of a rough, superficial summary in the sense of a list of topics and thus appear to be less suitable for the qualitative content analysis methods I developed (MAYRING, 2022a, 2022b).
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