Universität Wien
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210018 UE BAK3 Quantitative Methoden (2024W)

BAK3 Quantitative methods

6.00 ECTS (2.00 SWS), SPL 21 - Politikwissenschaft
Prüfungsimmanente Lehrveranstaltung

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Moodle

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Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Mittwoch 09.10. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 16.10. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 23.10. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 30.10. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 06.11. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 13.11. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 20.11. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 27.11. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 04.12. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 11.12. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 08.01. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 15.01. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 22.01. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
  • Mittwoch 29.01. 09:45 - 11:15 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is complementary to the theoretical course VO BAK3 “Quantitative Methoden” taught by Univ.-Prof. Markus Wagner (2024W) and is intended to deepen the content discussed there. It is therefore strongly recommended that students attend the lecture at the same time.

The aim of the course is to equip students with the basic applied skills needed to carry out easy data projects on their own. The content of the course includes basic descriptive and inferential statistics, as well as the graphic representation of results. The course design relies on practical exercises in the computer lab and interactive discussions. Students will revise the basics of empirical quantitative research methods and learn to apply the basic tools of quantitative data analysis using the open-source software R (with RStudio). It is recommended to install the necessary software (R, RStudio) on your own laptop before the start of the course. Both are available online free of charge.

By the end of the course, students should be able to describe and manipulate a dataset and conduct basic inferential analyses with R. Students should also be able to develop and answer research questions using quantitative methods and to present quantitative research findings appropriately.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The final assessment will be based on the following components:

1. Attendance/Participation (10%): regular attendance in class (maximum of 2 classes can be missed) and active participation in class activities such as software tasks and discussions. Students must be present for the first session (09.10.2024) or they will be deregistered from the course.

2. Homework assignments (25%): based on materials in the course. Students are allowed to work in groups, but assignments must be submitted individually.

3. A mid-term exam (25%): theoretical questions about quantitative methods of empirical social research and interpretation of R output.

4. Final assignment (40%): at the end of the course, you will be required to write a final paper of 2000-2500 words, focusing on quantitative methods (rather than theory) with applications in R. The paper is due on February 28th, 2025.

Mindestanforderungen und Beurteilungsmaßstab

Please note that all four components are essential for the final grade, i.e. regularly attending classes, handing in homework assignments, completing the mid-term exam, and submitting the final assignment. In cases of suspected plagiarism, you may be called upon to reasonably demonstrate that any work you have submitted is your own (the anti-plagiarism software Turnitin will be used via Moodle to detect plagiarism). A passing grade on each component is not required for a passing grade in the course overall.

Grading Scale:
90-100 points = 1 (excellent)
80-89 points = 2 (good)
70-79 points = 3 (satisfactory)
60-69 points = 4 (sufficient)
< 60 points = 5 (fail)

Prüfungsstoff

The mid-term exam will focus on different topics covered in class and will include basic data analysis using the R commands learnt in class. Detailed instructions about the homework, the exam and the final assignment will be shared on Moodle.

Literatur

Recommended:
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). Pearson Education International.
- Elena Llaudet & Kosuke Imai (2022). Data Analysis for Social Science. Princeton University Press.

Further readings will be announced in the course.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: So 22.09.2024 17:26