Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research
Summary: arXiv:2604.03820v1 Announce Type: new
Abstract: 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.
Introduction
As large language models (LLMs) gain traction in qualitative research, the need for transparency in analytic processes becomes paramount. Traditional qualitative analysis often lacks clarity in how conclusions are drawn, leading to potential biases and misunderstandings. QualAnalyzer addresses this gap by providing tools that ensure process auditability in qualitative data analysis.
What is QualAnalyzer?
QualAnalyzer is an innovative, open-source Chrome extension designed specifically for Google Workspace. Its primary function is to facilitate atomistic LLM analysis, which entails examining each segment of qualitative data independently. By doing so, the tool allows researchers to:
- Preserve the context of each analytical decision.
- Maintain a comprehensive log of prompts, inputs, and outputs.
- Foster a deeper understanding of the analytic process.
Case Studies
To demonstrate the capabilities of QualAnalyzer, we present two distinct case studies:
- Holistic Essay Scoring: In this case study, the tool was used to evaluate essays based on various criteria. The ability to document each step of the scoring process allowed for an in-depth analysis of how LLMs arrived at their conclusions compared to human raters.
- Deductive Thematic Coding of Interview Transcripts: The second case study involved coding qualitative data from interviews. QualAnalyzer facilitated the identification of themes by processing segments of text individually, enabling researchers to compare LLM outputs with human-generated codes.
Importance of Process Auditability
Process auditability is crucial for enhancing the credibility and reliability of qualitative research that employs LLMs. By providing a clear audit trail, QualAnalyzer allows researchers to:
- Identify and rectify biases in analytic processes.
- Engage in reflexive practices to better understand the influence of AI on qualitative outcomes.
- Provide transparent methodologies that can be replicated and scrutinized by peers.
Conclusion
In conclusion, QualAnalyzer emerges as a pivotal tool for researchers in qualitative fields seeking to incorporate LLMs into their workflows. By ensuring process auditability, it not only enhances transparency but also bolsters the methodological rigor of qualitative research. As the landscape of qualitative analysis continues to evolve with technology, tools like QualAnalyzer will be instrumental in guiding researchers towards more accountable and robust practices.