Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports atomistic LLM analysis by processing each data segment independently and preserving the prompt, input, and output for every unit. Through two case studies -- holistic essay scoring and deductive thematic coding of interview transcripts -- we show that this approach creates a legible audit trail and helps researchers investigate systematic differences between LLM and human judgments. We argue that process auditability is essential for making LLM-assisted qualitative research more transparent and methodologically robust.
@article{arxiv.2604.03820,
title = {Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research},
author = {Max Hao Lu and Ryan Ellegood and Rony Rodriguez-Ramirez and Sophia Blumert},
journal= {arXiv preprint arXiv:2604.03820},
year = {2026}
}