Empirical Research in Management and Economics is a Master's course that covers the most important aspects of empirical research, including research design, data collection, data analysis, and interpretation.
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R for Data Science is a Master's course that equips students with core data science skills such as data wrangling, data analysis, and programming techniques.
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Judgment and Decision Making is a Master's course that reviews central empirical findings in decision research and the key theoretical approaches to analyze, understand, and evaluate decision making.
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Models in the Study of Human Behavior is a Master's course that discusses the utility and limitations of formal modeling approaches in the study of human behavior.
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R for Data Science is a Master's course that equips students with core data science skills such as data wrangling, data analysis, and programming techniques.
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Introduction to Bayesian Data Analysis is a Master's course where students learn to learn with the help of Bayesian statistics and a modern scientific data analysis workflow.
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Replicability, Robustness, and Reproducibility in Behavioral Research is a Master's course that discusses the history and causes of the replication crisis as well as recent developments and proposals towards a more reliable, robust, and transparent science.
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Process Tracing is a Master's course that provides knowledge about existing process-tracing methods and their functionality regarding the measumerent of predecisional information search and processing.
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Bayesian Data Analysis and Cognitive Modeling is a PhD course that introduces Bayesian statistics as a tool for analysing data and estimating parameters of cognitive models using statistical software.
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