040331 KU Empirical Methods in Decision Sciences (MA) (2020S)
Continuous assessment of course work
Labels
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 Mo 10.02.2020 09:00 to We 19.02.2020 12:00
- Registration is open from Tu 25.02.2020 09:00 to We 26.02.2020 12:00
- Deregistration possible until Th 30.04.2020 23:59
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
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
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
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