LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research
This addresses the problem of expert-intensive manual coding in qualitative research, offering a scalable solution that could democratize the field.
The paper tackles the scalability bottleneck in grounded theory development by introducing LOGOS, an end-to-end framework that fully automates the workflow from raw text to structured hierarchical theory, achieving 88.2% alignment with expert-developed schemas on a complex dataset.
Grounded theory offers deep insights from qualitative data, but its reliance on expert-intensive manual coding presents a major scalability bottleneck. Current computational tools stop short of true automation, keeping researchers firmly in the loop. We introduce LOGOS, a novel, end-to-end framework that fully automates the grounded theory workflow, transforming raw text into a structured, hierarchical theory. LOGOS integrates LLM-driven coding, semantic clustering, graph reasoning, and a novel iterative refinement process to build highly reusable codebooks. To ensure fair comparison, we also introduce a principled 5-dimensional metric and a train-test split protocol for standardized, unbiased evaluation. Across five diverse corpora, LOGOS consistently outperforms strong baselines and achieves a remarkable $88.2\%$ alignment with an expert-developed schema on a complex dataset. LOGOS demonstrates a powerful new path to democratize and scale qualitative research without sacrificing theoretical nuance.