040331 KU Empirical Methods in Decision Sciences (MA) (2021S)
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
service email address: opim.bda@univie.ac.at
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 11.02.2021 09:00 bis Mo 22.02.2021 12:00
- Anmeldung von Do 25.02.2021 09:00 bis Fr 26.02.2021 12:00
- Abmeldung bis Mi 31.03.2021 23:59
Details
max. 33 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
05.05.21 - Written exam
Synchronous online teaching
- Mittwoch 03.03. 15:00 - 16:30 Digital (Vorbesprechung)
- Mittwoch 10.03. 15:00 - 16:30 Digital
- Mittwoch 17.03. 15:00 - 16:30 Digital
- Mittwoch 24.03. 15:00 - 16:30 Digital
- Mittwoch 14.04. 15:00 - 16:30 Digital
- Mittwoch 21.04. 15:00 - 16:30 Digital
- Mittwoch 28.04. 15:00 - 16:30 Digital
- Mittwoch 05.05. 15:00 - 16:30 Digital
- Mittwoch 12.05. 15:00 - 16:30 Digital
- Mittwoch 19.05. 15:00 - 16:30 Digital
- Mittwoch 26.05. 15:00 - 16:30 Digital
- Mittwoch 02.06. 15:00 - 16:30 Digital
- Mittwoch 09.06. 15:00 - 16:30 Digital
- Mittwoch 16.06. 15:00 - 16:30 Digital
- Mittwoch 23.06. 15:00 - 16:30 Digital
- Mittwoch 30.06. 15:00 - 16:30 Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
Written exam concerning theoretical design knowledge
oTree assignments
Data analysis assignments
oTree assignments
Data analysis assignments
Mindestanforderungen und Beurteilungsmaßstab
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
Prüfungsstoff
Course content
Literatur
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
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 12.05.2023 00:12