Gaining insight is considered one of the relevant purposes of visual data exploration, yet studies that categorize insights are rare. This paper reports on a study to understand if the categorization model used to describe insights and personality factors affect insight-based evaluations' findings. Participants completed a set of tasks with three hierarchical visualizations and then reported what insights they could gather from them. Results show that the insight categorization taxonomies produce different descriptions of insights based on the same corpus of responses. In addition, our findings suggest that the openness to experience trait positively influences the number of reported insights. Both these factors may create obstacles to the design of insight-based evaluations and, consequently, should be controlled in the experimental design. We discuss the study implications, lessons learned, and future work opportunities.

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Concepts and paradigms,Human-centered computing,Empirical studies in visualization,Visualization,Visualization theory