Dates
| weekly | Tuesday | 10:15 - 13:45 | 12.10.2026 - 29.01.2027 | C 11.320 Seminarraum |
Curriculum context
Resit date: No resit date will be offered to this assessment, because it is didactically inseparably connected with one of the associated courses. A resit will only be possible, if the module is available again.
Organizational information
Registration
Registration does not begin until 02.10.2026 at 08:00. It ends on 12.10.2026 at 23:59.
The registration is restricted to the following fields or courses of study:
- Leuphana Bachelor / Major Environmental and Sustainability Studies (bis Studienbeginn WiSe 25/26) / nur ab Semester 7
- Leuphana Bachelor / Major Global Environmental and Sustainability Studies (bis Studienbeginn WiSe 25/26) / nur ab Semester 5
- Leuphana Bachelor / Major Umweltwissenschaften (bis Studienbeginn WiSe 25/26) / nur ab Semester 5
- Leuphana Bachelor / Major Studium Individuale / nur ab Semester 3
- Leuphana Bachelor / Minor Studium Individuale / nur ab Semester 3
This course belongs to the cluster "Major_UWI_GESS_ENVI_Electives".
In this cluster, you have a maximum of 2 of choices during the lottery phases.
Persons
Content
Behavioural interventions are widely used to encourage sustainable behaviour and inform policy making. This course introduces the experimental methods behavioural science uses to test them, the statistics needed to analyse the results, and the practical R skills for carrying out those analyses.
Each week pairs a question from behavioural science, such as how to measure a behaviour, whether a difference between conditions is real, what an average effect can hide, with the statistical tool that answers it, implemented in R.
Each session has two parts: 90–100 minutes of teaching, followed by a short break and a practical R workshop. Students work with simulated intervention data, develop a hypothesis of their own, and report what the evidence does and does not support.
- translate a sustainability problem into a testable hypothesis about behaviour
- evaluate an experimental design: identify confounds, assess randomization, and judge whether the measured outcome reflects the behaviour of interest
- carry out a statistical analysis in R: import, clean, visualise, and model data
- interpret findings: state what the evidence from a study supports and what it does not support
Evaluation
Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/evaluation/course-evaluation.html