使用ChatGPT进行基础理论的实用指南和评估:教程教程
Yongjie Yue1, Dong Liu2, Yilin Lv2
1School of Journalism and Communication, Tsinghua University, Beijing, China.
Journal of medical Internet research
|May 14, 2025
概括
像ChatGPT这样的生成性大语言模型 (LLM) 可以提高定性数据分析效率和编码多样性在基础理论研究中. 虽然可靠,但LLM需要仔细应用,以解决与手动编码相比的深度和上下文限制.
科学领域:
- 人工智能的人工智能
- 定性数据分析 数据分析.
- 一个有根据的理论.
背景情况:
- 生成型大语言模型 (LLM) 显示了增强定性数据分析的前景.
- 在基础理论方法学中LLM的应用需要进一步的研究.
研究的目的:
- 提供早期洞察力,了解LLM如何在定性分析中提高文本编码的效率和可靠性.
- 在一个有基础的理论框架内评估ChatGPT 4-Turbo的性能.
主要方法:
- 一个逐步的教程,将ChatGPT 4-Turbo应用于接地理论方法.
- 对ChatGPT 4-Turbo编码结果与由定性分析软件辅助的手动编码进行比较分析.
- 利用与盲人游戏玩家进行半结构化访谈的数据集.
主要成果:
- 在许多方面,ChatGPT 4-Turbo的可靠性与手动编码相美.
- 法学士应用提高了编码效率和多样性,更新了基础理论过程.
- 与手动方法相比,在深度,上下文,连接和编码组织方面发现了缺陷.
结论:
- LLM,特别是ChatGPT 4-Turbo,为提高定性数据分析效率和编码提供了有价值的工具.
- 需要仔细考虑LLM的局限性,才能有效地融入定性研究.
- 讨论了在定性研究中应用人工智能的建议.
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