René Lehmann
Prof. Dr. Dr. René Lehmann
Chair in Empirical Economics
Education
| Since 12/2021 | Research Associate at the Chair of Empirical Economics (part-time) |
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Otto von Guericke University Magdeburg |
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| 09/2024 |
Doctorate, Dr. rer. pol. |
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| Since 03/2016 |
Professor of Business Mathematics and Statistics |
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FOM University of Applied Sciences for Economics and Management |
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| 04/2009 – 02/2016 |
Research Associate in IT Unit IV 2.1 |
| German Environment Agency: | |
| 10/2010 – 03/2012 | External doctoral candidate (private, part-time) |
| RWTH Aachen University | |
| 03/2012 |
Dr. rer. nat. |
| Faculty of Mathematics, Computer Science and Natural Sciences | |
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10/2001 – 02/2008 |
Studies in Business Mathematics (Diploma) Otto von Guericke University Magdeburg |
Fields of Interest
Psychometric statistics of Likert scales, Development and validation of Likert scales, Biostatistics and toxicology (uptake & degradation, ECx, NOAEC/LOAEC), Compositional data statistics
Current Research Projects
Taking compositional data structures into account can improve the analysis of Likert data. In this context, in addition to the degree of agreement with a statement, the implicitly given degree of non-agreement is also considered. Considering both pieces of information reveals that Likert data have a compositional data structure, which requires specific statistical analysis methods. The research project addresses the question of how the response format, for example the number of discrete response options, slider scales, or Thurstonian scales, affects the compositional data approach. Does the statistical power and/or predictive performance of correlation-based methods change?
Selected Topic-Related Publications
Lehmann, R., Vogt, B. (2025). Improving correlation as a measure of similarity of congener patterns through compositional data analysis. Environmental Sciences Europe. 37:192. DOI 10.1186/s12302-025-01250-2.
Lehmann, R. Vogt, B. (2025): Breakdown of the compositional data approach in psychometric Likert scale big data analysis: about the loss of statistical power of two-sample t-tests applied to heavy-tailed big data. Brain Informatics Journal, DOI 10.1186/s40708-025-00253-2.
Lehmann, R. Vogt, B. (2024): Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach. Brain Informatics Journal, 11, 19 (2024). https://doi.org/10.1186/s40708-024-00232-z
Lehmann, R., Bachmann, J., Karaoglan, B., Lacker, J., Lurman, G., Polleichtner, C., Ratte, H. T., Ratte, M. (2018): The CPCAT as a novel tool to overcome the shortcomings of NOEC/LOEC statistics in ecotoxicology: a simulation study to evaluate the statistical power. Environmental Sciences Europe. doi: 10.1186/s12302-018-0178-5.
Ottermanns, R, Cramer, E., Daniels, B., Lehmann, R., Roß-Nickoll, M. (2018): Uncertainty in site classification and its sensitivity to sample size and indicator quality – Bayesian misclassification rate. Ecological Indicators, 94, 348–356.
Lehmann, R., Bachmann, J., Karaoglan, B., Lacker, J., Polleichtner, C., Ratte, H. T., Ratte, M. (2017): An alternative approach to overcome shortcomings with multiple testing of binary data in ecotoxicology. Stochastic Environmental Research and Risk Assessment, DOI:10.1007/s00477-017-1392-1.
Lehmann, R., Bachmann, J., Maletzki, D., Polleichtner, C., Ratte, H. T., Ratte, M. (2016): A New Approach to Overcome Shortcomings With Multiple Testing of Reproduction Data in Ecotoxicology. Stochastic Environmental Research and Risk Assessment, 30(3), 871–882, DOI 10.1007/s00477-015-1079-4.
Ranke, J., Lindenberger, K., Lehmann, R. (2012); R software package mkin 0.9.2; http://cran.r-project.org/web/packages/mkin/