Prof. Dr. Michael Bosnjak is head of the Department of Psychological Research Methods. His research interests include: Research synthesis methods, AI-based inference methods, survey methodology, open science, and consumer/business psychology.

Professor Bosnjak is Editor-in-Chief of Zeitschrift für Psychologie, the oldest Psychology journal in Europe (founded in 1890 by Hermann Ebbinghaus and Arthur König).

From July 2017 until December 2021, he was Director of the Leibniz Institute for Psychology (ZPID) in Trier, Germany. In 2022, he was delegated to Robert Koch Institute in Berlin (Germany) as Scientific Director and Department Head (Epidemiology and Health Monitoring).

Before joining the University of Trier in 2017, he was team leader for the area Survey Operations at GESIS - Leibniz Institute for the Social Sciences in Mannheim, Germany, and Full Professor for Evidence-Based Survey Methodology at the University of Mannheim, School of Social Sciences. Between 2013 and 2016, he was the founding team leader of the GESIS Panel, a probabilistic mixed-mode omnibus panel for the social sciences.

Previously, he held positions as an Associate Professor of Marketing at the Free University of Bozen-Bolzano, Italy (2008-2015), as an Assistant Professor of Consumer Psychology and Research Methods at the University of Mannheim, Germany (2003-2008), as a research associate at ZUMA, the Center for Survey Research and Methodology in Mannheim (1998-2003), and as a graduate research assistant at the University of Heidelberg (1997-1998).

Prof. Dr. Michael Bosnjak received his master degree (German: Diplom) in Psychology from the University of Freiburg (Germany) in 1997 with a thesis on Internet-based studies in Psychology. He earned a Ph.D. in Psychology with a dissertation on ´Nonresponse in Web Surveys´ (2002, summa cum laude, University of Mannheim), and is holding his ´venia legendi´ for Psychology since 2007 (University of Mannheim).

In June 2013, he was awarded a Honorary Professorship at the University of Mannheim, School of Social Sciences, and in December 2013, he was awarded the Italian National Habilitation for Management (Full Professor level).

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Background: Some COVID-19 testing centres have reported manipulated test numbers for antigen tests/rapid tests. This study compares statistical approaches with traditional fraud detection methods. The extent of agreement between traditional and statistical methods was analysed, as well as the extent to which statistical approaches can identify additional cases of potential fraud. Methods: Outlier detection marking a high number of tests, modeling of the positivity rate (Poisson Regression), deviation from distributional assumptions regarding the first digit (Benford’s Law) and the last digit of the number of reported tests. The basis of the analyses were billing data (April 2021 to August 2022) from 907 testing centres in a German city. Results: The positive agreement between the conventional and statistical approaches (‘sensitivity’) was between 8.6% and 24.7%, the negative agreement (‘specificity’) was between 91.3% and 94.6%. The proportion of potentially fraudulent testing centres additionally identified by statistical approaches was between 7.0% and 8.7%. The combination of at least two statistical methods resulted in an optimal detection rate of test centres with previously undetected initial suspicion. Conclusions: The statistical approaches were more effective and systematic in identifying potentially fraudulent testing centres than the conventional methods. Testing centres should be urged to map paradata (e.g. timestamps of testing) in future pandemics.

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