IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v39y2019i3p183-195.html

Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

Author

Listed:
  • Yasmina Okan

    (Centre for Decision Research, Leeds University Business School, University of Leeds, Leeds, UK)

  • Eva Janssen

    (Department of Work and Social Psychology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands)

  • Mirta Galesic

    (Santa Fe Institute, Santa Fe, NM, USA
    Harding Center for Risk Literacy, Max Planck Institute for Human Development, Berlin, Germany)

  • Erika A. Waters

    (Washington University School of Medicine, Division of Public Health Sciences, Saint Louis, MO, USA)

Abstract

Background. Visual displays can facilitate risk communication and promote better health choices. Their effectiveness in improving risk comprehension is influenced by graph literacy. However, the construct of graph literacy is still insufficiently understood, partially because existing objective measures of graph literacy are either too difficult or too long. Objectives. We constructed a new 4-item Short Graph Literacy (SGL) scale and examined how SGL scores relate to key cognitive, affective, and conative precursors of health behavior change described in common health behavior theories. Methods. We performed secondary analyses to adapt the SGL scale from an existing 13-item scale. The initial construction was based on data collected in a laboratory setting in Germany ( n = 51). The scale was then validated using data from nationally representative samples in Germany ( n = 495) and the United States ( n = 492). To examine how SGL scores relate to precursors of health behavior change, we performed secondary analyses of a third study involving a nationwide US sample with 47% participants belonging to racial/ethnic minorities and 46% with limited formal education ( n = 835). Results. Graph literacy was significantly associated with cognitive precursors in theoretically expected ways (e.g., positive associations with risk comprehension and response efficacy and a negative association with cognitive risk perception). Patterns for affective precursors generally mirrored those for cognitive precursors, although numeracy was a stronger predictor than graph literacy for some affective factors (e.g., feelings of risk). Graph literacy had predictive value for most cognitive and affective precursors beyond numeracy. In addition, graph literacy (but not numeracy) predicted key conative precursors such as defensive processing. Conclusions. Our data suggest that the SGL scale is a fast and psychometrically valid method for measuring objective graph literacy. Our findings also highlight the theoretical and practical relevance of graph literacy.

Suggested Citation

  • Yasmina Okan & Eva Janssen & Mirta Galesic & Erika A. Waters, 2019. "Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change," Medical Decision Making, , vol. 39(3), pages 183-195, April.
    • Handle: RePEc:sae:medema:v:39:y:2019:i:3:p:183-195
    DOI: 10.1177/0272989X19829728
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X19829728
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X19829728?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. William J. Burns & Ellen Peters & Paul Slovic, 2012. "Risk Perception and the Economic Crisis: A Longitudinal Study of the Trajectory of Perceived Risk," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 659-677, April.
    3. Stone, Eric R. & Sieck, Winston R. & Bull, Benita E. & Frank Yates, J. & Parks, Stephanie C. & Rush, Carolyn J., 2003. "Foreground:background salience: Explaining the effects of graphical displays on risk avoidance," Organizational Behavior and Human Decision Processes, Elsevier, vol. 90(1), pages 19-36, January.
    4. Nathan F. Dieckmann & Paul Slovic & Ellen M. Peters, 2009. "The Use of Narrative Evidence and Explicit Likelihood by Decisionmakers Varying in Numeracy," Risk Analysis, John Wiley & Sons, vol. 29(10), pages 1473-1488, October.
    5. Christina Kreuzmair & Michael Siegrist & Carmen Keller, 2016. "High Numerates Count Icons and Low Numerates Process Large Areas in Pictographs: Results of an Eye‐Tracking Study," Risk Analysis, John Wiley & Sons, vol. 36(8), pages 1599-1614, August.
    6. Rebecca Hess & Vivianne H. M. Visschers & Michael Siegrist, 2011. "Risk communication with pictographs: The role of numeracy and graph processing," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(3), pages 263-274, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Teigen, Karl Halvor & Juanchich, Marie & Løhre, Erik, 2022. "What is a “likely” amount? Representative (modal) values are considered likely even when their probabilities are low," Organizational Behavior and Human Decision Processes, Elsevier, vol. 171(C).
    2. Erika A. Waters & Jennifer M. Taber & Nicole Ackermann & Julia Maki & Amy M. McQueen & Laura D. Scherer, 2023. "Testing Explanations for Skepticism of Personalized Risk Information," Medical Decision Making, , vol. 43(4), pages 430-444, May.
    3. A. Kause & W. Bruine de Bruin & J. Persson & H. Thorén & L. Olsson & A. Wallin & S. Dessai & N. Vareman, 2022. "Confidence levels and likelihood terms in IPCC reports: a survey of experts from different scientific disciplines," Climatic Change, Springer, vol. 173(1), pages 1-18, July.
    4. Marie-Anne Durand & Renata W Yen & James O’Malley & Glyn Elwyn & Julien Mancini, 2020. "Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a Medicaid eligible population," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Deborah A. Marshall & Jorien Veldwijk & Ellen M. Janssen & Shelby D. Reed, 2025. "Stated-Preference Survey Design and Testing in Health Applications," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 18(3), pages 187-197, May.
    6. Lyndal J. Trevena & Carissa Bonner & Yasmina Okan & Ellen Peters & Wolfgang Gaissmaier & Paul K. J. Han & Elissa Ozanne & Danielle Timmermans & Brian J. Zikmund-Fisher, 2021. "Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts," Medical Decision Making, , vol. 41(7), pages 834-847, October.
    7. Engler, Ben H. & Hutzler, Florian & Hawelka, Stefan, 2025. "An extended logarithmic visualization improves forecasting accuracy for exponentially growing numbers, but residual difficulties remain," International Journal of Forecasting, Elsevier, vol. 41(2), pages 466-474.
    8. Dirk Leffrang & Oliver Müller, 2025. "Visualizing Uncertainty in Time Series Forecasts: The Impact of Uncertainty Visualization on Users' Confidence, Algorithmic Advice Utilization, and Forecasting Performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1235-1246, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shoots-Reinhard, Brittany & Goodwin, Raleigh & Bjälkebring, Pär & Markowitz, David M. & Silverstein, Michael C. & Peters, Ellen, 2021. "Ability-related political polarization in the COVID-19 pandemic," Intelligence, Elsevier, vol. 88(C).
    2. Garcia-Retamero, Rocio & Hoffrage, Ulrich, 2013. "Visual representation of statistical information improves diagnostic inferences in doctors and their patients," Social Science & Medicine, Elsevier, vol. 83(C), pages 27-33.
    3. Nathan F. Dieckmann & Robin Gregory & Ellen Peters & Robert Hartman, 2017. "Seeing What You Want to See: How Imprecise Uncertainty Ranges Enhance Motivated Reasoning," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 471-486, March.
    4. Carmen Keller & Christina Kreuzmair & Rebecca Leins-Hess & Michael Siegrist, 2014. "Numeric and graphic risk information processing of high and low numerates in the intuitive and deliberative decision modes: An eye-tracker study," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(5), pages 420-432, September.
    5. Cornelia Betsch & Niels Haase & Frank Renkewitz & Philipp Schmid, 2015. "The narrative bias revisited: What drives the biasing influence of narrative information on risk perceptions?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(3), pages 241-264, May.
    6. Floris Goerlandt & Jie Li & Genserik Reniers, 2020. "The Landscape of Risk Communication Research: A Scientometric Analysis," IJERPH, MDPI, vol. 17(9), pages 1-31, May.
    7. Lyndal J. Trevena & Carissa Bonner & Yasmina Okan & Ellen Peters & Wolfgang Gaissmaier & Paul K. J. Han & Elissa Ozanne & Danielle Timmermans & Brian J. Zikmund-Fisher, 2021. "Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts," Medical Decision Making, , vol. 41(7), pages 834-847, October.
    8. Yasmina Okan & Eric R. Stone & Wändi Bruine de Bruin, 2018. "Designing Graphs that Promote Both Risk Understanding and Behavior Change," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 929-946, May.
    9. Wang, Xunxiao & Wu, Chongfeng, 2018. "Asymmetric volatility spillovers between crude oil and international financial markets," Energy Economics, Elsevier, vol. 74(C), pages 592-604.
    10. Nathan F. Dieckmann & Ellen Peters & Robin Gregory, 2015. "At Home on the Range? Lay Interpretations of Numerical Uncertainty Ranges," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1281-1295, July.
    11. Firth, Chris & Stewart, Neil & Antoniou, Constantinos & Leake, David, 2023. "The effects of personality and IQ on portfolio outcomes," Finance Research Letters, Elsevier, vol. 51(C).
    12. Arunachalam Narayanan & Brent B. Moritz, 2015. "Decision Making and Cognition in Multi-Echelon Supply Chains: An Experimental Study," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1216-1234, August.
    13. Irina Gemmo & Pierre-Carl Michaud & Olivia S. Mitchell, 2023. "Selection into Financial Education and Effects on Portfolio Choice," NBER Working Papers 31682, National Bureau of Economic Research, Inc.
    14. Ray Saadaoui Mallek & Mohamed Albaity, 2019. "Individual differences and cognitive reflection across gender and nationality the case of the United Arab Emirates," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1567965-156, January.
    15. Reynolds, J.P. & Pilling, M. & Marteau, T.M., 2018. "Communicating quantitative evidence of policy effectiveness and support for the policy: Three experimental studies," Social Science & Medicine, Elsevier, vol. 218(C), pages 1-12.
    16. Paul K. J. Han & William M. P. Klein & Tom Lehman & Bill Killam & Holly Massett & Andrew N. Freedman, 2011. "Communication of Uncertainty Regarding Individualized Cancer Risk Estimates," Medical Decision Making, , vol. 31(2), pages 354-366, March.
    17. Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2023. "On the benefits of robo-advice in financial markets," Working Papers 0734, University of Heidelberg, Department of Economics.
    18. Christine Laudenbach & Michael Ungeheuer & Martin Weber, 2023. "How to Alleviate Correlation Neglect in Investment Decisions," Management Science, INFORMS, vol. 69(6), pages 3400-3414, June.
    19. William J. Burns & Ellen Peters & Paul Slovic, 2012. "Risk Perception and the Economic Crisis: A Longitudinal Study of the Trajectory of Perceived Risk," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 659-677, April.
    20. Dong, Lu & Huang, Lingbo & Lien, Jaimie W. & Zheng, Jie, 2024. "How alliances form and conflict ensues," Games and Economic Behavior, Elsevier, vol. 146(C), pages 255-276.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:39:y:2019:i:3:p:183-195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.