Measuring risk literacy: The Berlin Numeracy Test
AbstractWe introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies (n=5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers (www.riskliteracy.org). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.
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Bibliographic InfoArticle provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.
Volume (Year): 7 (2012)
Issue (Month): 1 (January)
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risk literacy; statistical numeracy; individual differences; cognitive abilities; quantitative reasoning; decision making; risky choice; adaptive testing; Mechanical Turk.;
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- James Banks & Cormac O'Dea & Zoë Oldfield, 2010. "Cognitive Function, Numeracy and Retirement Saving Trajectories," Economic Journal, Royal Economic Society, vol. 120(548), pages F381-F410, November.
- Gretchen B. Chapman & Jingjing Liu, 2009. "Numeracy, frequency, and Bayesian reasoning," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(1), pages 34-40, February.
- Saima Ghazal & Edward T. Cokely & Rocio Garcia-Retamero, 2014. "Predicting biases in very highly educated samples: Numeracy and metacognition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 15-34, January.
- 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.
- Sundar, B. & Virmani, Vineet, . "Numeracy and Financial Literacy of Forest Dependent Communities Evidence from Andhra Pradesh," IIMA Working Papers WP2013-09-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
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