Designing Computational Tools for Exploring Causal Relationships in Qualitative Data

TL;DR

This paper presents QualCausal, a novel computational tool that helps researchers explore and visualize causal relationships in qualitative data, addressing limitations of existing systems by considering context and providing user-friendly visualizations.

Contribution

The paper introduces QualCausal, a system designed based on user needs to extract and visualize causal relationships in qualitative data, enhancing analysis in HCI and social sciences.

Findings

Participants found the system reduced analytical effort.

The system provided valuable cognitive scaffolding.

Navigation within established research practices was a challenge.

Abstract

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.