In this paper, we argue that readability cannot be meaningfully discussed without considering multiple complementary measures, and that relying on a single measure constitutes an epistemological choice that constrains the conclusions that can be drawn.
What do different readability measures actually allow researchers to claim about the ease of reading?
We examine how readability is currently specified in research on visualization reading and discuss why acknowledging the measurement choices is needed for interpreting empirical results.
A.-F. Cabouat, S. Huron, T. Isenberg, and P. Isenberg, “Readability as a multi-measure construct in data visualization,” in CHI 2026 STAR Workshop – Science and Technology for Augmenting Reading, Barcelona, Spain, 2026. Available: https://hal.science/hal-05548028