Data Visualization Made Simple: Insights into Becoming Visual
Abstract
Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The amount and format of represented data can impact how users process information and decision quality.
“Drawing from the field of visual analytics, the amount and format of the represented data of an AI-based system, such as a dashboard, can have a significant impact on how users process the information and how good the jointly reached decisions are”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Sosulski, 2018
The format of data visualization affects how users interpret the underlying data and thereby influences their decision-making process.
“The format of data visualization affects how users interpret the underlying data and thereby influences their decision-making process (Endsley, 2017; Sosulski, 2018).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Sosulski, 2018
Cited by
Sorted by citations
Check the box! How to deal with automation bias in AI-based personnel selection
Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1118723 · Cited by 59 · see citing works
“Drawing from the field of visual analytics, the amount and format of the represented data of an AI-based system, such as a dashboard, can have a significant impact on how users process the information and how good the jointly reached decisions are (Endsley, 2017; Sosulski, 2018).”
BackgroundMethod
- Authors
- Sosulski, K.
- Corresponding author
- Sosulski, Kristen
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2018-09-27
- OpenAlex
- W4241040519
- Cited by
- 12
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 2 · 2022: 3 · 2020: 1 · 2019: 1
- FWCI
- 0.44
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- data visualization, data graphics, visualization design, visualization literacy, decision-making, information sharing, learning analytics, big data, visualization process, exploratory visualization
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