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Testing Perceptual Accuracy in a U.S. General Population Survey Using Stacked Bar Charts
Volume 22, Issue 2 (2024): Special Issue: 2023 Symposium on Data Science and Statistics (SDSS): “Inquire, Investigate, Implement, Innovate”, pp. 280–297
Kiegan Rice ORCID icon link to view author Kiegan Rice details   Heike Hofmann ORCID icon link to view author Heike Hofmann details   Nola du Toit ORCID icon link to view author Nola du Toit details     All authors (4)

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https://doi.org/10.6339/24-JDS1121
Pub. online: 13 March 2024      Type: Statistical Data Science      Open accessOpen Access

Received
31 July 2023
Accepted
2 February 2024
Published
13 March 2024

Abstract

The use of visuals is a key component in scientific communication. Decisions about the design of a data visualization should be informed by what design elements best support the audience’s ability to perceive and understand the components of the data visualization. We build on the foundations of Cleveland and McGill’s work in graphical perception, employing a large, nationally-representative, probability-based panel of survey respondents to test perception in stacked bar charts. Our findings provide actionable guidance for data visualization practitioners to employ in their work.

Supplementary material

 Supplementary Material
The source code and data used in this paper and an example stimulus image are available on a GitHub repository at https://github.com/kiegan/testing-charts-jds.

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
data visualization graphical perception survey data

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