开发交互式工具帮助研究者从定性数据中发现因果关系。
Designing Computational Tools for Exploring Causal Relationships in Qualitative Data
- 通过构建因果网络与多视图可视化提取因果关系
- 15人反馈显示系统减轻分析负担并提供认知支持
- 适合人机交互与社会科学领域研究者使用
在人机交互与社会科学研究中，探索定性数据的因果关系有助于理解用户需求和理论构建。然而，现有计算工具主要聚焦于数据分类，少数具备因果分析能力的系统或忽略上下文、可信度不足，或输出过于复杂。我们通过15名参与者的形式化研究，了解其需求并提炼设计准则。基于此，设计并实现QualCausal系统，通过交互式因果网络构建与多视图可视化展示因果关系。15人反馈研究表明，参与者认可该系统减轻分析负担并提供认知支撑，但对如何融入既有研究范式与习惯仍存困惑。本文探讨了支持定性数据分析的计算工具设计的更广泛启示。
原文摘要 · Abstract (English)
Exploring causal relationships for qualitative data analysis in HCI and social science research enables the understanding of user needs and theory building. However, current computational tools primarily characterize and categorize qualitative data; the few systems that analyze causal relationships either inadequately consider context, lack credibility, or produce overly complex outputs. We first conducted a formative study with 15 participants interested in using computational tools for exploring causal relationships in qualitative data to understand their needs and derive design guidelines. Based on these findings, we designed and implemented QualCausal, a system that extracts and illustrates causal relationships through interactive causal network construction and multi-view visualization. A feedback study (n = 15) revealed that participants valued our system for reducing the analytical burden and providing cognitive scaffolding, yet navigated how such systems fit within their established research paradigms, practices, and habits. We discuss broader implications for designing computational tools that support qualitative data analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。