Publication Date
2-28-2026
Faculty Department
Department of Health Sciences
Document Type
Article
Abstract
Qualitative data analysis, particularly thematic analysis, is a widely used method for uncovering patterns and insights in narrative data but often faces challenges such as being resource-intensive and susceptible to researcher bias. In recent years, Large Language Models (LLMs) have emerged as promising tools to assist human coders in conducting thematic analysis, offering efficiency and scalability in processing large datasets. However, existing work primarily relies on tools like ChatGPT, raising privacy concerns—especially when analyzing sensitive healthcare data—and lacks systematic validation through comparisons with human coders. This study aims to evaluate the potential of open-source LLMs for qualitative data analysis. Semi-structured patient interviews (n = 34) conducted by a trained qualitative researcher provided the dataset, with traditional qualitative (inductive) analysis by two researchers serving as the gold standard. Two open-source LLMs, Gemma2 and Llama3.1, were used to generate codes, and their performance was assessed based on factors such as chunk size, inductive vs. deductive methods, and prompting approaches. Results showed some alignment between LLM- and researcher-generated codes, with the deductive approach yielding higher and more nuanced insights (Gemma2: n = 723; Llama3.1: n = 1,042) compared to the inductive approach (Gemma2: n = 715; Llama3.1: n = 829). Approximately 45% of the LLM-generated codes provided meaningful context, though 22–39% were duplicative. While LLMs demonstrated efficiency in analyzing large volumes of textual data, nearly half of the codes lacked sufficient context or were repetitive. Our findings showed that although combining traditional qualitative analysis with AI presents a promising avenue, future research should explore how pre-trained LLMs could augment qualitative analysis with higher inter-rater reliability to improve patient-provider communication. There is a need for further research or the development of domain-specific LLMs to improve their utility in qualitative analysis.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Misra, R., Dahal, R., Kirk, B., Khan, R., Dogan, G., Chataut, R., & Gyawali, P. (2026). Large Language Models in Qualitative Analysis: Comparing Traditional and Researcher-Interpreted Approaches. International Journal of Qualitative Methods, 25. https://doi.org/10.1177/16094069261426100 (Original work published 2026)
