Causality-Based Visual Analysis of Questionnaire Responses

Renzhong Li, Weiwei Cui, Tianqi Song, Xiao Xie, Rui Ding, Yun Wang, Haidong Zhang, Hong Zhou, Yingcai Wu

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2023-10-25T05:33:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
The interface of QE: (A) The question list view displays all questions. (B) The question combination view provides an overview of the whole dataset. (C) The causal view presents the causality in a relevant question combination. (D) The respondent view visualizes the clusters of respondents divided by a set of relevant questions for users to deep dive.
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Causal analysis, Questionnaire, Design study


As the final stage of questionnaire analysis, causal reasoning is the key to turning responses into valuable insights and actionable items for decision-makers. During the questionnaire analysis, classical statistical methods (e.g., Differences-in-Differences) have been widely exploited to evaluate causality between questions. However, due to the huge search space and complex causal structure in data, causal reasoning is still extremely challenging and time-consuming, and often conducted in a trial-and-error manner. On the other hand, existing visual methods of causal reasoning face the challenge of bringing scalability and expert knowledge together and can hardly be used in the questionnaire scenario. In this work, we present a systematic solution to help analysts effectively and efficiently explore questionnaire data and derive causality. Based on the association mining algorithm, we dig question combinations with potential inner causality and help analysts interactively explore the causal sub-graph of each question combination. Furthermore, leveraging the requirements collected from the experts, we built a visualization tool and conducted a comparative study with the state-of-the-art system to show the usability and efficiency of our system.