Evaluating convergence between two data visualization literacy assessments

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 constru...

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Main Authors: Erik Brockbank, Arnav Verma, Hannah Lloyd, Holly Huey, Lace Padilla, Judith E. Fan
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:Cognitive Research
Subjects:
Online Access:https://doi.org/10.1186/s41235-025-00622-9
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author Erik Brockbank
Arnav Verma
Hannah Lloyd
Holly Huey
Lace Padilla
Judith E. Fan
author_facet Erik Brockbank
Arnav Verma
Hannah Lloyd
Holly Huey
Lace Padilla
Judith E. Fan
author_sort Erik Brockbank
collection DOAJ
description 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.
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spelling doaj-art-d6c5fabda8b3432ead3e1e412855697b2025-08-20T01:52:59ZengSpringerOpenCognitive Research2365-74642025-04-0110111410.1186/s41235-025-00622-9Evaluating convergence between two data visualization literacy assessmentsErik Brockbank0Arnav Verma1Hannah Lloyd2Holly Huey3Lace Padilla4Judith E. Fan5Department of Psychology, Stanford UniversityDepartment of Psychology, Stanford UniversityDepartment of Psychology, University of California San DiegoDepartment of Psychology, University of California San DiegoDepartment of Computer Science, Northeastern UniversityDepartment of Psychology, Stanford UniversityAbstract 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.https://doi.org/10.1186/s41235-025-00622-9Graph comprehensionGraphical literacyData literacyPsychometric evaluationSTEM education
spellingShingle Erik Brockbank
Arnav Verma
Hannah Lloyd
Holly Huey
Lace Padilla
Judith E. Fan
Evaluating convergence between two data visualization literacy assessments
Cognitive Research
Graph comprehension
Graphical literacy
Data literacy
Psychometric evaluation
STEM education
title Evaluating convergence between two data visualization literacy assessments
title_full Evaluating convergence between two data visualization literacy assessments
title_fullStr Evaluating convergence between two data visualization literacy assessments
title_full_unstemmed Evaluating convergence between two data visualization literacy assessments
title_short Evaluating convergence between two data visualization literacy assessments
title_sort evaluating convergence between two data visualization literacy assessments
topic Graph comprehension
Graphical literacy
Data literacy
Psychometric evaluation
STEM education
url https://doi.org/10.1186/s41235-025-00622-9
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