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Graphical perception of value distributions : an evaluation of non-expert viewers’ data literacy
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Zubiaga, Arkaitz and MacNamee, Brian (2016) Graphical perception of value distributions : an evaluation of non-expert viewers’ data literacy. Journal of Community Informatics, 12 (3). ISSN 1712-4441.
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Official URL: http://www.ci-journal.net/index.php/ciej/article/v...
Abstract
An ability to understand the outputs of data analysis is a key characteristic of data literacy and the inclusion of data visualisations is ubiquitous in the output of modern data analysis. Several aspects still remain unresolved, however, on the question of choosing data visualisations that lead viewers to an optimal interpretation of data. This is especially true when audiences have differing degrees of data literacy. In this paper we describe two user studies on perception from data visualisations, in which we measured the ability of participants to validate statements about the distributions of data samples visualised using different chart types. In the first user study, we find that histograms are the most suitable chart type for illustrating the distribution of values for a variable. We contrast our findings with previous research in the field, and posit three main issues identified from the study. Most notably, however, we show that viewers struggle to identify scenarios in which a chart simply does not contain enough information to validate a statement about the data that it represents. In the follow-up study, we ask viewers questions about quantification of frequencies, and identification of most frequent values from different types of histograms and density traces showing one or two distributions of values. This study reveals that viewers do better with histograms when they need to quantify the values displayed in a chart. Among the different types of histograms, interspersing the bars of two distributions in a histogram leads to the most accurate perception. Even though interspersing bars makes them thinner, the advantage of having both distributions clearly visible pays off. The findings of these user studies provide insight to assist designers in creating optimal charts that enable comparison of distributions, and emphasise the importance of using an understanding of the limits of viewers’ data literacy to design charts effectively.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HA Statistics | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Statistics -- Charts, diagrams, etc., Statistics -- Graphic methods | ||||||||
Journal or Publication Title: | Journal of Community Informatics | ||||||||
Publisher: | Journal of Community Informatics | ||||||||
ISSN: | 1712-4441 | ||||||||
Official Date: | 2016 | ||||||||
Dates: |
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Volume: | 12 | ||||||||
Number: | 3 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 25 July 2016 | ||||||||
Date of first compliant Open Access: | 1 December 2016 | ||||||||
Funder: | Ireland. Industrial Development Agency, Enterprise Ireland, Ireland. Centre for Applied Data Analytics (CEADAR) | ||||||||
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