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A Practical Guide and Assessment on Using ChatGPT to Conduct Grounded Theory: Tutorial.

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Generative large language models (LLMs) like ChatGPT can improve qualitative data analysis efficiency and coding diversity in grounded theory research. While reliable, LLMs require careful application to address limitations in depth and context compared to manual coding.

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Area of Science:

  • Artificial Intelligence
  • Qualitative Data Analysis
  • Grounded Theory

Background:

  • Generative large language models (LLMs) show promise for enhancing qualitative data analysis.
  • The application of LLMs in grounded theory methodology requires further investigation.

Purpose of the Study:

  • To provide an early insight into how LLMs can enhance the efficiency and reliability of text coding in qualitative analysis.
  • To evaluate the performance of ChatGPT 4-Turbo within a grounded theory framework.

Main Methods:

  • A step-by-step tutorial applying ChatGPT 4-Turbo to a grounded theory approach.
  • Comparative analysis of ChatGPT 4-Turbo coding results against manual coding assisted by qualitative analysis software.
  • Utilizing a dataset of semistructured interviews with blind gamers.

Main Results:

  • ChatGPT 4-Turbo demonstrated reliability comparable to manual coding in many aspects.
  • LLM application enhanced coding efficiency and diversity, updating the grounded theory process.
  • Shortcomings were noted in depth, context, connections, and coding organization compared to manual methods.

Conclusions:

  • LLMs, specifically ChatGPT 4-Turbo, offer a valuable tool for improving qualitative data analysis efficiency and coding.
  • Careful consideration of LLM limitations is necessary for effective integration into qualitative research.
  • Recommendations for applying artificial intelligence in qualitative research were discussed.