Universität Wien FIND

Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Vor-Ort-Lehre und Umstellung auf Online-Prüfungen) erforderlich sein. Melden Sie sich für Lehrveranstaltungen/Prüfungen über u:space an, informieren Sie sich über den aktuellen Stand auf u:find und auf der Lernplattform moodle. ACHTUNG: Lehrveranstaltungen, bei denen zumindest eine Einheit vor Ort stattfindet, werden in u:find momentan mit "vor Ort" gekennzeichnet.

Regelungen zum Lehrbetrieb vor Ort inkl. Eintrittstests finden Sie unter https://studieren.univie.ac.at/info.

210160 UE BAK4.2 UE Quantitative Methoden der empirischen Sozialforschung (2020W)

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

Die Lehrformate für das WS (digital, hybrid, vor Ort) befinden sich in Entwicklung. Die Lehrenden werden die geplante Organisationsform und Lehrmethodik auf ufind und Moodle bekannt geben. Aufgrund von Covid19 muss mit kurzfristigen Änderungen in Richtung digitaler Lehre gerechnet werden.

Nicht-prüfungsimmanente (n-pi) Lehrveranstaltung. Eine Anmeldung über u:space ist erforderlich. Mit der Anmeldung werden Sie automatisch für die entsprechende Moodle-Plattform freigeschaltet. Vorlesungen unterliegen keinen Zugangsbeschränkungen.

VO-Prüfungstermine erfordern eine gesonderte Anmeldung.
Mit der Teilnahme an der Lehrveranstaltung verpflichten Sie sich zur Einhaltung der Standards guter wissenschaftlicher Praxis. Schummelversuche und erschlichene Prüfungsleistungen führen zur Nichtbewertung der Lehrveranstaltung (Eintragung eines 'X' im Sammelzeugnis).

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first serve").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

This course will be held online, instruction is in English and students are expected to submit their work in English as well. The course uses the statistical programming language R.

Donnerstag 08.10. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 15.10. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 22.10. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 29.10. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 05.11. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 12.11. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 19.11. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 26.11. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 03.12. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 10.12. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 17.12. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 07.01. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 14.01. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 21.01. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital
Donnerstag 28.01. 13:15 - 14:45 Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is complementary to the theoretical course “210014 VO BAK 4 Quantitative methods in the empirical social sciences (2020W)” taught by Professor Markus Wagner. The aim of the course is to equip students with the basic applied skills for easy data projects. The content of the course includes descriptive univariate (scale levels, position and dispersion measures, frequency tables) and bivariate (cross tables, correlation measures for different scale levels) analysis methods, as well as the graphic representation of results and the basics of inferential regression statistics. The core focus of is course will be hands-on and practical. The 210014 VO lecture component will cover more abstract ideas. Students are strongly encouraged to attend the lecture as well.

Students will learn the basic “tools” to conduct quantitative data analysis, using the statistical software R. Theoretical concepts of descriptive and inferential statistics will be briefly discussed in class, in combination with their practical application using existing databases typical of those in the field of political science. By the end of the course, you should be able to describe a dataset and conduct basic inferential analysis using the main commands implemented in R.

At the end of the course, students should know and understand the basic methods and simple statistical procedures in the social sciences, as well as be able to interpret and evaluate the results of quantitative social research in research and the media. You should also be able to develop questions yourself and answer them using quantitative methods and be able to present the results of quantitative research appropriately.

The primary method of the course will be digital/online using Moodle and BigBlueButton. This will allow for the maximum number of students to attend synchronously.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The final assessment will be based on the following components:
(1) Attendance/Participation (10% of final grade) Regular attendance in class (maximum 2 classes can be missed)
(2) 3 short homework assignments (25% of final grade) based on materials in the course texts. Students are encouraged to form study groups but assignments must be completed individually. The Turnitin program will ensure that no plagiarism occurs.
(3) 1 short test (25% of final grade). The test will be conducted in class and will concern theoretical questions and/or interpretation of R output. Duration: max 45 minutes.
(4) Final assignment (40% of final grade). At the end of the course, you will be required to write a final paper of 2000-2500 words, focusing mostly on methods with applications in R. Detailed instructions about the final assignment will be posted on Moodle and circulated in class before the end of the course. Joint work is NOT allowed for the final assignment. Deadline for handing in the final assignment: 31 March 2020.

Final grades will be a summation of these:
100-90 Points Excellent (1)
89-80 Points Good (2)
79-70 Points Satisfactory (3)
69-60 Points Sufficient (4)
59-0 Points Insufficient (5)

Mindestanforderungen und Beurteilungsmaßstab

Please note that all four components are essential for the final grade, i.e. you have to be present in class, hand in 3 homework assignments, complete the short test, and hand in the final assignment. In cases of suspected plagiarism, you may be called upon to reasonably demonstrate that any work they you have submitted is your own. A passing grade on each component is not required for a passing grade in the course.

Prüfungsstoff

The examination will focus on different statistical concepts covered in class and will include basic data analysis using the programming language R. Detailed instructions about the homework assignments and the final assignment will be posted on Moodle in due time.

Literatur

The following readings are required:
- Garrett Grolemund and Hadley Wickham. R for Data Science. https://r4ds.had.co.nz/
- James Long and Paul Teetor. R cookbook (2nd edition) https://rc2e.com/

Suggested optional readings:
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). New Jersey: Pearson Education International
- Kosuke Imai, Quantitative Social Science: An Introduction, Princeton University Press, 2018.
- Paul M. Kellstedt, and Guy D. Whitten. 2018 (3rd edition). The fundamentals of political science research. Cambridge: Cambridge University Press

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 05.10.2020 10:10