Qualitative data analysis provides insight into the underlying perceptions and experiences within unstructured data. However, the time-consuming nature of the coding process, especially for larger datasets, calls for innovative approaches, such as the integration of Large Language Models (LLMs). This short paper presents initial findings from a study investigating the integration of LLMs for coding tasks of varying complexity in a real-world dataset. Our results highlight the challenges inherent in coding with extensive codebooks and contexts, both for human coders and LLMs, and suggest that the integration of LLMs into the coding process requires a task-by-task evaluation. We examine factors influencing the complexity of coding tasks and initiate a discussion on the usefulness and limitations of incorporating LLMs in qualitative research.

翻译：定性数据分析能够揭示非结构化数据中的潜在感知与经验。然而，编码过程耗时巨大，尤其是在处理大规模数据集时，亟需创新方法，例如集成大型语言模型（LLMs）。本短文报告了一项针对真实世界数据集中不同复杂度编码任务中LLMs集成效果研究的初步发现。结果揭示了在复杂编码手册和语境下，人类编码员与LLMs共同面临的固有挑战，并指出将LLMs融入编码过程需逐项任务评估。我们探讨了影响编码任务复杂度的因素，并就LLMs在定性研究中的效用与局限性展开讨论。