Issue |
ITM Web Conf.
Volume 67, 2024
The 19th IMT-GT International Conference on Mathematics, Statistics and Their Applications (ICMSA 2024)
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Article Number | 01017 | |
Number of page(s) | 8 | |
Section | Mathematics, Statistics and Their Applications | |
DOI | https://doi.org/10.1051/itmconf/20246701017 | |
Published online | 21 August 2024 |
Navigating Visual Information: Understanding Audience Perception and Evaluation in Different Data Visualizations
Faculty of Science, Universiti Tunku Abdul Rahman, 31900 Kampar, Perak, Malaysia
* Corresponding author: looisy@utar.edu.my
In the age of Big Data where data literacy is vital across diverse domains, the prevalence of misleading visualization raises significant concerns. Examining the extent of such visualizations is crucial since viewers often lack the ability to choose the form of presentation. This study aims to investigate the impact of intentionally misleading data visualizations on cognitive biases by exploring factors that potentially influence perceptions and evaluations. A factorial experimental design with three factors involving methods of data visualization, audience academic background and the sequence of data presentation is employed. A total of 60 undergraduate students with two different major programmes from a local higher educational institution participated in this experiment. These students were tasked with responding to predesigned questions based on two different sets of infographics addressing the same issues. The findings indicate that both data presentation and analytical background significantly influence audience evaluation. Additionally, the order of data presentation reveals that audience evaluation is influenced by their initial negative impression. These results underscore the critical role of data literacy in enhancing the understanding of visual information, particularly in the context of public issues.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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