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Interpretations of Data in Ethical v...

O'Brien, Shaun.

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Interpretations of Data in Ethical vs. Unethical Data Visualizations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interpretations of Data in Ethical vs. Unethical Data Visualizations./
Author:	O'Brien, Shaun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	83 p.
Notes:	Source: Masters Abstracts International, Volume: 57-01.
Contained By:	Masters Abstracts International57-01(E).
Subject:	Technical communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10681043
ISBN:	9780355510850

Interpretations of Data in Ethical vs. Unethical Data Visualizations.
O'Brien, Shaun.

Interpretations of Data in Ethical vs. Unethical Data Visualizations. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 83 p.

Source: Masters Abstracts International, Volume: 57-01.

Thesis (M.S.)--Arizona State University, 2017.

This paper presents the results of an empirical analysis of deceptive data visualizations paired with explanatory text. Data visualizations are used to communicate information about important social issues to large audiences and are found in the news, social media, and the Internet (Kirk, 2012). Modern technology and software allow people and organizations to easily produce and publish data visualizations, contributing to data visualizations becoming more prevalent as a means of communicating important information (Sue & Griffin, 2016). Ethical transgressions in data visualizations are the intentional or unintentional use of deceptive techniques with the potential of altering the audience's understanding of the information being presented (Pandey et al., 2015). While many have discussed the importance of ethics in data visualization, scientists have only recently started to look at how deceptive data visualizations affect the reader. This study was administered as an on-line user survey and was designed to test the deceptive potential of data visualizations when they are accompanied by a paragraph of text. The study consisted of a demographic questionnaire, chart familiarity assessment, and data visualization survey. A total of 256 participants completed the survey and were evenly distributed between a control (non-deceptive) survey or a test (deceptive) survey in which participant were asked to observe a paragraph of text and data visualization paired together. Participants then answered a question relevant to the observed information to measure how they perceived the information to be. The individual differences between demographic groups and their responses were analyzed to understand how these groups reacted to deceptive data visualizations compared to the control group. The results of the study confirmed that deceptive techniques in data visualizations caused participants to misinterpret the information in the deceptive data visualizations even when they were accompanied by a paragraph of explanatory text. Furthermore, certain demographics and comfort levels with chart types were more susceptible to certain types of deceptive techniques. These results highlight the importance of education and practice in the area of data visualizations to ensure deceptive practices are not utilized and to avoid potential misinformation, especially when information can be called into question.

ISBN: 9780355510850Subjects--Topical Terms:

3172863
Technical communication.

Interpretations of Data in Ethical vs. Unethical Data Visualizations.
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245 1 0 $a Interpretations of Data in Ethical vs. Unethical Data Visualizations.
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300 $a 83 p.
500 $a Source: Masters Abstracts International, Volume: 57-01.
500 $a Adviser: Claire Laure.
502 $a Thesis (M.S.)--Arizona State University, 2017.
520 $a This paper presents the results of an empirical analysis of deceptive data visualizations paired with explanatory text. Data visualizations are used to communicate information about important social issues to large audiences and are found in the news, social media, and the Internet (Kirk, 2012). Modern technology and software allow people and organizations to easily produce and publish data visualizations, contributing to data visualizations becoming more prevalent as a means of communicating important information (Sue & Griffin, 2016). Ethical transgressions in data visualizations are the intentional or unintentional use of deceptive techniques with the potential of altering the audience's understanding of the information being presented (Pandey et al., 2015). While many have discussed the importance of ethics in data visualization, scientists have only recently started to look at how deceptive data visualizations affect the reader. This study was administered as an on-line user survey and was designed to test the deceptive potential of data visualizations when they are accompanied by a paragraph of text. The study consisted of a demographic questionnaire, chart familiarity assessment, and data visualization survey. A total of 256 participants completed the survey and were evenly distributed between a control (non-deceptive) survey or a test (deceptive) survey in which participant were asked to observe a paragraph of text and data visualization paired together. Participants then answered a question relevant to the observed information to measure how they perceived the information to be. The individual differences between demographic groups and their responses were analyzed to understand how these groups reacted to deceptive data visualizations compared to the control group. The results of the study confirmed that deceptive techniques in data visualizations caused participants to misinterpret the information in the deceptive data visualizations even when they were accompanied by a paragraph of explanatory text. Furthermore, certain demographics and comfort levels with chart types were more susceptible to certain types of deceptive techniques. These results highlight the importance of education and practice in the area of data visualizations to ensure deceptive practices are not utilized and to avoid potential misinformation, especially when information can be called into question.
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