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Measuring Graph Literacy without a Test

Author

Listed:
  • Rocio Garcia-Retamero
  • Edward T. Cokely
  • Saima Ghazal
  • Alexander Joeris

Abstract

Background . Visual aids tend to help diverse and vulnerable individuals understand risk communications, as long as these individuals have a basic understanding of graphs (i.e., graph literacy). Tests of objective graph literacy (OGL) can effectively identify individuals with limited skills, highlighting vulnerabilities and facilitating custom-tailored risk communication. However, the administration of these tests can be time-consuming and may evoke negative emotional reactions (e.g., anxiety). Objectives . To evaluate a brief and easy-to-use assessment of subjective graph literacy (SGL) (i.e., self-reported ability to process and use graphically presented information) and to estimate the robustness and validity of the SGL scale and compare it with the leading OGL scale in diverse samples from different cultures. Participants . Demographically diverse residents ( n = 470) of the United States, young adults ( n = 172) and patients (n = 175) from Spain, and surgeons ( n = 175) from 48 countries. Design . A focus group and 4 studies for instrument development and initial validation (study 1), reliability and convergent and discriminant validity evaluation (study 2), and predictive validity estimation (studies 3 and 4). Measures . Psychometric properties of the scale. Results . In about 1 minute, the SGL scale provides a reliable, robust, and valid assessment of skills and risk communication preferences and evokes fewer negative emotional reactions than the OGL scale. Conclusions . The SGL scale can be suitable for use in clinical research and may be useful as a communication aid in clinical practice. Theoretical mechanisms involved in SGL, emerging applications, limitations, and open questions are discussed.

Suggested Citation

  • Rocio Garcia-Retamero & Edward T. Cokely & Saima Ghazal & Alexander Joeris, 2016. "Measuring Graph Literacy without a Test," Medical Decision Making, , vol. 36(7), pages 854-867, October.
    • Handle: RePEc:sae:medema:v:36:y:2016:i:7:p:854-867
    DOI: 10.1177/0272989X16655334
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    References listed on IDEAS

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    1. Edward T. Cokely & Colleen M. Kelley, 2009. "Cognitive abilities and superior decision making under risk: A protocol analysis and process model evaluation," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(1), pages 20-33, February.
    2. Gabriele Paolacci & Jesse Chandler & Panagiotis G. Ipeirotis, 2010. "Running experiments on Amazon Mechanical Turk," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(5), pages 411-419, August.
    3. Edward T. Cokely & Mirta Galesic & Eric Schulz & Saima Ghazal & Rocio Garcia-Retamero, 2012. "Measuring risk literacy: The Berlin Numeracy Test," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(1), pages 25-47, January.
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    2. Bruinsma Bastiaan, 2020. "Evaluating Visualisations in Voting Advice Applications," Statistics, Politics and Policy, De Gruyter, vol. 11(1), pages 1-21, June.

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