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ChatGPT for Automated Qualitative Research: Content Analysis.

Rimke Bijker1, Stephanie S Merkouris2, Nicki A Dowling2

  • 1Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand.

Journal of Medical Internet Research
|July 25, 2024
PubMed
Summary

ChatGPT shows promise in assisting qualitative content analysis, particularly in developing inductive coding schemes. It can be a reliable tool for researchers, but further iterations are needed to confirm its utility in all analysis stages.

Keywords:
ChatGPTTheoretical Domains Frameworknatural language processingqualitative content analysis

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Area of Science:

  • Computational Social Science
  • Artificial Intelligence in Research
  • Qualitative Data Analysis

Background:

  • Qualitative content analysis is time-consuming, involving data detection, assessment, and coding.
  • Large language models like ChatGPT offer potential for automating aspects of qualitative data analysis.

Purpose of the Study:

  • To evaluate ChatGPT's effectiveness in performing qualitative content analysis.
  • To analyze forum posts on sugar reduction experiences using ChatGPT.

Main Methods:

  • Inductive and deductive content analysis applied to 537 forum posts.
  • Prompt engineering used to guide ChatGPT in data analysis tasks.
  • Comparison of ChatGPT's extracted data with human coding for precision assessment.

Main Results:

  • Precision of change mechanism detection ranged from 66% to 88%.
  • Intercoder agreement scores (κ) varied, with inductive schemes showing higher agreement (0.72–0.82).
  • ChatGPT demonstrated reliability in following instructions, especially for inductive coding scheme development.

Conclusions:

  • ChatGPT is a fairly reliable assistant for qualitative content analysis.
  • It excels in developing inductive coding schemes from data compared to deductive approaches.
  • ChatGPT shows potential as a second coder, warranting further investigation for reliability across analysis stages.