Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research
For qualitative researchers, this tool addresses the need for transparency and methodological robustness in LLM-assisted analysis by enabling process auditability.
QualAnalyzer is a Chrome extension for atomistic LLM analysis in qualitative research, preserving prompt-input-output for each data segment to create a legible audit trail. Case studies on essay scoring and thematic coding show it helps investigate systematic differences between LLM and human judgments.
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.