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Do Icon Arrays Help Reduce Denominator Neglect?

Author

Listed:
  • Rocio Garcia-Retamero

    (Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany, Department of Experimental Psychology, University of Granada, Spain, rretamer@ugr.es)

  • Mirta Galesic

    (Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany)

  • Gerd Gigerenzer

    (Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany, Harding Center for Risk Literacy, Berlin, Germany)

Abstract

Background and Objective. Denominator neglect is the focus on the number of times a target event has happened (e.g., the number of treated and nontreated patients who die) without considering the overall number of opportunities for it to happen (e.g., the overall number of treated and nontreated patients). In 2 studies, we addressed the effect of denominator neglect in problems involving treatment risk reduction where samples of treated and non-treated patients and the relative risk reduction were of different sizes. We also tested whether using icon arrays helps people take these different sample sizes into account. We especially focused on older adults, who are often more disadvantaged when making decisions about their health. Design. Study 1 was conducted on a laboratory sample using a within-subjects design; study 2 was conducted on a nonstudent sample interviewed through the Web using a between-subjects design. Outcome Measures. Accuracy of understanding risk reduction. Results. Participants often paid too much attention to numerators and insufficient attention to denominators when numerical information about treatment risk reduction was provided. Adding icon arrays to the numerical information, however, drew participants’ attention to the denominators and helped them make more accurate assessments of treatment risk reduction. Icon arrays were equally helpful to younger and older adults. Conclusions. Building on previous research showing that problems with understanding numerical information often do not reside in the mind but in the representation of the problem, the results show that icon arrays are an effective method of eliminating denominator neglect.

Suggested Citation

  • Rocio Garcia-Retamero & Mirta Galesic & Gerd Gigerenzer, 2010. "Do Icon Arrays Help Reduce Denominator Neglect?," Medical Decision Making, , vol. 30(6), pages 672-684, November.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:6:p:672-684
    DOI: 10.1177/0272989X10369000
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    1. repec:cup:judgdm:v:17:y:2022:i:2:p:378-399 is not listed on IDEAS
    2. Myeong-Hun Jeong & Matt Duckham & Susanne Bleisch, 2015. "Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    3. Christina Kreuzmair & Michael Siegrist & Carmen Keller, 2017. "Does Iconicity in Pictographs Matter? The Influence of Iconicity and Numeracy on Information Processing, Decision Making, and Liking in an Eye‐Tracking Study," Risk Analysis, John Wiley & Sons, vol. 37(3), pages 546-556, March.
    4. repec:cup:judgdm:v:14:y:2019:i:1:p:1-10 is not listed on IDEAS
    5. 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.
    6. Paul C. Price & Grace A. Carlock & Sarah Crouse & Mariana Vargas Arciga, 2022. "Effects of icon arrays to communicate risk in a repeated risky decision-making task," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(2), pages 378-399, March.
    7. 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.
    8. Ian G. J. Dawson & Johnnie E. V. Johnson & Michelle A. Luke, 2013. "Helping Individuals to Understand Synergistic Risks: An Assessment of Message Contents Depicting Mechanistic and Probabilistic Concepts," Risk Analysis, John Wiley & Sons, vol. 33(5), pages 851-865, May.
    9. Richard B. Anderson & Laura Marie Leventhal & Don C. Zhang & Daniel Fasko, Jr. & Zachariah Basehore & Christopher Gamsby & Jared Branch & Timothy Patrick, 2019. "Belief bias and representation in assessing the Bayesian rationality of others," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(1), pages 1-10, January.
    10. 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.
    11. Eric R. Stone & Wändi Bruine de Bruin & Abigail M. Wilkins & Emily M. Boker & Jacqueline MacDonald Gibson, 2017. "Designing Graphs to Communicate Risks: Understanding How the Choice of Graphical Format Influences Decision Making," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 612-628, April.
    12. 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.
    13. Schlosser, Ann E., 2018. "What are my chances? An imagery versus discursive processing approach to understanding ratio-bias effects," Organizational Behavior and Human Decision Processes, Elsevier, vol. 144(C), pages 112-124.

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