Universität Wien

040331 KU Empirical Methods in Decision Sciences (MA) (2023S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

service email address: opim.bda@univie.ac.at
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 33 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 01.03. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock (Kickoff Class)
  • Wednesday 08.03. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 15.03. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 22.03. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 29.03. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 19.04. 15:00 - 16:30 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
    Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 26.04. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 03.05. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 10.05. 15:00 - 16:30 Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
    Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 17.05. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 24.05. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 31.05. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 07.06. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 14.06. 15:00 - 16:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 21.06. 15:00 - 16:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
    Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

In this course students are introduced to decision science relevant empirical methods. The major focus point of the course is the design and implementation of computerized interactive and static experiments. Students are introduced to the theoretical knowledge about various experimental designs, practical implementation of the designed experiment using Python language based platform and analysis of the results using Python & R based tools. Principal interest in computer programming can be helpful for this course. The course is taught in English.

Assessment and permitted materials

Written exam concerning theoretical design knowledge
oTree assignments
Data analysis assignments

Minimum requirements and assessment criteria

Students need a laptop for oTree and data analysis assignments.
All partial achievements (exam & assignments) must be positive in order to have a positive grade from the course.
0-50 points => 5
51-63 points => 4
64-75 points => 3
76-87 points => 2
88-100 points => 1

Examination topics

Course content

Reading list

Douglas, C. M. (2019). Design analysis of Experiments. John Wiley & Sons
Donohue, K., Katok, E., & Leider, S. (Eds.). (2018). The handbook of behavioral operations. John Wiley & Sons.
Guttag, J. V. (2013). Introduction to computation and programming using Python. Mit Press.
For oTree: https://otree.readthedocs.io/en/latest/
For Python: https://www.python.org
For JupyterLab: https://jupyterlab.readthedocs.io/en/stable/
For R: https://www.r-project.org

Association in the course directory

Last modified: Tu 14.03.2023 11:28