Face to Face: Evaluating Visual Comparison

Brian D. Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri

Abstract: Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.