  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.
