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Journal of Data Science


Evaluating Perceptual Judgements on 3D Printed Bar Charts
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 176–190
Pub. online: 24 May 2024      Type: Data Science In Action      Open accessOpen Access
Received
1 August 2023
Accepted
7 April 2024
Published
24 May 2024

Abstract

Graphical design principles typically recommend minimizing the dimensionality of a visualization - for instance, using only 2 dimensions for bar charts rather than providing a 3D rendering, because this extra complexity may result in a decrease in accuracy. This advice has been oft repeated, but the underlying experimental evidence is focused on fixed 2D projections of 3D charts. In this paper, we describe an experiment which attempts to establish whether the decrease in accuracy extends to 3D virtual renderings and 3D printed charts. We replicate the grouped bar chart comparisons in the 1984 Cleveland & McGill study, assessing the accuracy of numerical estimates using different types of 3D and 2D renderings.

Supplementary material

 Supplementary Material
Stimuli, code, and data for this experiment are provided at https://github.com/TWiedRW/2023-JDS-3dcharts.

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Copyright
2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
graphics 3D bar charts 3D printing

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