Graphical Perception of Value Distributions: An Evaluation of Non-Expert Viewers Data Literacy
|Title:||Graphical Perception of Value Distributions: An Evaluation of Non-Expert Viewers Data Literacy||Authors:||Zubiaga, Arkaitz
|Permanent link:||http://hdl.handle.net/10197/8324||Date:||6-Jun-2016||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, and when the aim is to make sure that members of a community, who may differ on background and expertise, will make similar interpretations from data visualisations. 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.||Funding Details:||Enterprise Ireland||Type of material:||Journal Article||Publisher:||Journal of Community Informatics||Journal:||Journal of Community Informatics||Volume:||12||Issue:||3||Start page:||138||End page:||159||Copyright (published version):||2016 the Authors||Keywords:||Machine learning; Statistics||Other versions:||http://ci-journal.net/index.php/ciej/article/view/1275||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Insight Research Collection|
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