QuAD: Quantitative Assessment via Distribution-Alignment for Qualitative Data Quality Enabled by a Content-Aware Multi-Agent System

Key points

• Assessing whether qualitative responses collected from semi-structured questions are ready for a particular analysis goal is a persistent challenge.1
• QuAD compares a reconstructed intrinsic cluster distribution reconstructed from responses with an extrinsic, researcher-defined analytical lens derived from metadata.1
• Because this assessment relies on lens-conditioned inter-response dissimilarity, we propose a content-aware multi-agent system with two-stage instruction generation and adaptive anchors to improve scoring consistency.1
• We validate QuAD through controlled experiments on synthetic data, including robustness and baseline comparisons, and a real-world social survey case study.1

Sources (1)

• [1]QuAD: Quantitative Assessment via Distribution-Alignment for Qualitative Data Quality Enabled by a Content-Aware Multi-Agent System
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:46 AM

  We introduce QuAD, a framework for quantitative pre-analysis assessment of qualitative response datasets.

  Assessing whether qualitative responses collected from semi-structured questions are ready for a particular analysis goal is a persistent challenge.

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