Data visualization

By Manuela Aparicio, C. Costa. CDQR, 2015

402 citations recorded by the source corpus.

Data visualization—a set of approaches for applying graphical principles to represent quantitative information—is extremely well matched to the nature of survey data but often underleveraged for this purpose. Surveys produce data sets that are highly structured and comparative across groups and geographies, that often blend numerical and open-text information, and that are designed for repeated administration and analysis. Each of these characteristics aligns well with specific visualization types, use of which has the potential to—when paired with foundational, evidence-based tenets of high-quality graphical representations—substantially increase the impact and influence of data presentations given by survey researchers. This chapter recommends and provides guidance on data visualization techniques fit to purpose for survey researchers, while also describing key risks and missteps associated with these approaches.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0a71cf69b891d07e49b63cb2b24d392eb68299f8

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Amir Hussain
2. Mufti Mahmud
3. Simone Scardapane
4. Kaizhu Huang
5. Laura Palagi

Citations in the corpus

• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “One of the approaches to finding influential instances is called deletion diagnostics [152].”