Universität Wien

040331 KU Empirical Methods in Decision Sciences (MA) (2021S)

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

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

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

05.05.21 - Written exam
Synchronous online teaching

Wednesday 03.03. 15:00 - 16:30 Digital (Kickoff Class)
Wednesday 10.03. 15:00 - 16:30 Digital
Wednesday 17.03. 15:00 - 16:30 Digital
Wednesday 24.03. 15:00 - 16:30 Digital
Wednesday 14.04. 15:00 - 16:30 Digital
Wednesday 21.04. 15:00 - 16:30 Digital
Wednesday 28.04. 15:00 - 16:30 Digital
Wednesday 05.05. 15:00 - 16:30 Digital
Wednesday 12.05. 15:00 - 16:30 Digital
Wednesday 19.05. 15:00 - 16:30 Digital
Wednesday 26.05. 15:00 - 16:30 Digital
Wednesday 02.06. 15:00 - 16:30 Digital
Wednesday 09.06. 15:00 - 16:30 Digital
Wednesday 16.06. 15:00 - 16:30 Digital
Wednesday 23.06. 15:00 - 16:30 Digital
Wednesday 30.06. 15:00 - 16:30 Digital

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: Fr 12.05.2023 00:12