Evaluating convergence between two data visualization literacy assessments.

Erik Brockbank, Arnav Verma, Hannah Lloyd, Holly Huey, Lace Padilla, Judith E Fan

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Article in Cognitive research: principles and implications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Erik BrockbankDepartment of Psychology, Stanford University, Stanford, USA. ebrockbank@stanford.edu.ORCID 0000-0001-8702-239X

Arnav VermaDepartment of Psychology, Stanford University, Stanford, USA.

Hannah LloydDepartment of Psychology, University of California San Diego, La Jolla, USA.

Holly HueyDepartment of Psychology, University of California San Diego, La Jolla, USA.

Lace PadillaDepartment of Computer Science, Northeastern University, Boston, USA.

Judith E FanDepartment of Psychology, Stanford University, Stanford, USA.

Funding

Division of Behavioral and Cognitive Sciences 2047191Division of Behavioral and Cognitive Sciences 2122174Division of Behavioral and Cognitive Sciences 2404706Division of Information and Intelligent Systems 2238175Division of Research on Learning in Formal and Informal Settings 2400471

6 · The paper itself

Abstract

Data visualizations play a crucial role in communicating patterns in quantitative data, making data visualization literacy a key target of STEM education. However, it is currently unclear to what degree different assessments of data visualization literacy measure the same underlying constructs. Here, we administered two widely used graph comprehension assessments (Galesic and Garcia-Retamero in Med Dec Mak 31:444-457, 2011; Lee et al. in IEEE Trans Vis Comput Graph 235:51-560, 2016) to both a university-based convenience sample and a demographically representative sample of adult participants in the USA (N=1,113). Our analysis of individual variability in test performance suggests that overall scores are correlated between assessments and associated with the amount of prior coursework in mathematics. However, further exploration of individual error patterns suggests that these assessments probe somewhat distinct components of data visualization literacy, and we do not find evidence that these components correspond to the categories that guided the design of either test (e.g., questions that require retrieving values rather than making comparisons). Together, these findings suggest opportunities for development of more comprehensive assessments of data visualization literacy that are organized by components that better account for detailed behavioral patterns.

Indexed as

ComprehensionData VisualizationEducational MeasurementLiteracyAdolescentAdultFemaleHumansMaleMiddle AgedYoung AdultData literacyGraph comprehensionGraphical literacyPsychometric evaluationSTEM education

Identifiers

PMID40188224

DOI10.1186/s41235-025-00622-9

PMCPMC11972256

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LicenceCC BY

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