IEEE VIS 2024 Content: PREVis: Perceived Readability Evaluation for Visualizations

Honorable Mention

PREVis: Perceived Readability Evaluation for Visualizations

Anne-Flore Cabouat - LISN, Université Paris Saclay, CNRS, Orsay, France. Aviz, Inria, Saclay, France

Tingying He - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Petra Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Tobias Isenberg - Université Paris-Saclay, CNRS, Orsay, France. Inria, Saclay, France

Room: Bayshore I + II + III

2024-10-18T12:54:00Z GMT-0600 Change your timezone on the schedule page
2024-10-18T12:54:00Z
Exemplar figure, described by caption below
PREVis is a reliable instrument that allows respondents to rate how readable they find a static data visualization across 4 dimensions: layout clarity, ease of understanding, ease of reading data features, and ease of reading data values.
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Keywords

Visualization, readability, validated instrument, perception, user experiments, empirical methods, methodology

Abstract

We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.