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040331 KU Empirical Methods in Decision Sciences (MA) (2021S)
Continuous assessment of course work
Labels
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).
- Registration is open from Th 11.02.2021 09:00 to Mo 22.02.2021 12:00
- Registration is open from Th 25.02.2021 09:00 to Fr 26.02.2021 12:00
- Deregistration possible until We 31.03.2021 23:59
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
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
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
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