Large Language Models for Qualitative Data Analysis

Abstract

Qualitative data analysis has long relied on human interpretation, thematic coding, and iterative sense-making, making it both intellectually rich and methodologically challenging. The rapid emergence of Large Language Models (LLMs) introduces a new paradigm for analyzing unstructured qualitative data at scale while preserving contextual depth. This paper investigates the role of LLMs in qualitative data analysis, examining their capacity to perform tasks such as thematic extraction, coding consistency, sentiment interpretation, and narrative synthesis. Through controlled experiments on interview transcripts, open-ended survey responses, and textual field notes, we evaluate the analytical performance of LLMs against traditional qualitative analysis methods. Results demonstrate that LLMs significantly enhance efficiency and reproducibility while maintaining high semantic fidelity. However, limitations related to interpretability, bias propagation, and epistemic transparency remain. This study positions LLMs not as replacements for qualitative researchers, but as intelligent analytical collaborators that reshape how qualitative inquiry is conducted in data-intensive research environments.