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RiskLiteracy.org is a non-profit university-based academic project that uses the Berlin Numeracy Test to provide feedback about risk literacy and statistical literacy.  Initially developed and validated at the Max Planck Institute for Human Development, the Berlin Numeracy Test provides a fast and psychometrically sound instrument for assessment of statistical numeracy and risk literacy. The Berlin Numeracy Test was created to help increase public awareness and to improve research conducted with commonly used samples from diverse cultures and backgrounds (e.g., computer literate adults; educated people from the US, Europe, and Asia; highly educated medical, legal, and financial professionals).

Because some test formats are internet-based and available in multiple languages, data can be easily collected and analyzed by nearly any internet-ready device (e.g., smart phones; computers), providing immediate and personalized feedback.  In short,  RiskLiteracy.org can be used by individuals who want to learn more about their abilities and limitations, or it can be used by researchers seeking to gather and score data using a fast, validated, and reliable psychometric instrument.


Project Management and Development:

This project was initiated in 2007, at the Max Planck Institute for Human Development, under the direction of Dr. Edward Cokely (Michigan Technological University), Dr. Mirta Galesic (Max Planck Institute for Human Development), and Dr. Rocio Garcia-Retamero (University of Granada).  Riskliteracy.org is managed and maintained by members of the Decision Science and Decision Engineering Laboratory (DeSciDE) at Michigan Technological University.  For questions and comments please contact us.

Special Thanks and Financial Support:

We thank Dr. Gerd Gigerenzer, Dr. Lael Schooler, and other members of the Center for Adaptive Behavior and Cognition and of the Harding Center for Risk Literacy at the Max Planck Institute for Human Development.  We also thank Dr. Bruce Seely, Dean of Sciences and Arts, and Dr. Brad Baltensperger, Chair of Cognitive and Learning Sciences at the Michigan Technological University

We are grateful for grant support provided by Dr. Jean Bethke Elshtain, Dr. Howard Nusbaum, Dr. Barnaby Marsh, and many others from the the Arete Initiative and the Center for Cognitive and Social Neuroscience at the University of Chicago, the New Sciences of Virtues Project, and the John Templeton Foundation.  We also thank Time Sharing Experiments in the Social Sciences and the U.S. National Science Foundation, and the the Spanish Ministry of Science and Innovation.


Many Thanks:
We are indebted to the following researchers for cross-cultural and other data collection/analysis:
(in alphabetical order)

Nicolai Bodemer, Max Planck Institute for Human Development
Siegfried Dewitte, Katholieke Universiteit Leuven
Adam Feltz, Schreiner University
Robert Hamm, University of Oklahoma
Natasha Hagadone, Michigan Technological University
William (Deak) Helton, University of Canterbury
Stefan Herzog, University of Basel
Marcus Lindskog, Uppsala University
Hitashi Lomash, Thepar University
Yasmina Okan, University of Granada
Robert Pastel, Michigan Technological University
Dafina Petrova, University of Granada
Jing Qian, Tsinghua University
Samantha Simon, Wayne State University
Helena Szrek, University of Porto
Masanori Takezawa, University of Tokyo
Karl Teigen, University of Oslo
Margo Woller-Carter, Michigan Technological University
Jan Woike, Universite de Lausanne
Tomek Wysocki, Wroclaw University 







 The Berlin Numeracy Test
In 21 studies (n=5336) we found robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk). Analyses demonstrated desirable patterns of convergent validity (e.g., cognitive ability), 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).