Universität Wien FIND
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040331 KU Empirical Methods in Decision Sciences (MA) (2020S)

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

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

Wednesday 04.03. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 11.03. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 18.03. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 25.03. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 01.04. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 22.04. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 29.04. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 06.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 13.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 20.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 27.05. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 03.06. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 10.06. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 17.06. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Wednesday 24.06. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.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.

The programming languages and tools used in the course are platform independent, nevertheless it is highly recommended to bring a laptop for students’ own convenience.

Assessment and permitted materials

Written exam concerning theoretical design knowledge
oTree assignments
Data analysis assignments

Minimum requirements and assessment criteria

Participation in the first class is mandatory!
First class will be held in SR 16.
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: Mo 07.09.2020 15:19