Universität Wien
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230136 UE Specific Multivariate Methods of Analysis in the Social Sciences (2023W)

4.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

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

Details

max. 22 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Freitag 13.10. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Freitag 20.10. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Freitag 03.11. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Freitag 17.11. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Freitag 24.11. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Samstag 25.11. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33
  • Freitag 01.12. 09:45 - 13:15 Seminarraum 6 UniCampus Hof 7 Eingang 7.1 OG01 2H-O1-33

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Focusing on the practice of empirical research in the social sciences, this course is intended to provide the basic in multivariate data analyses. The aim of the course is to provide students with fundamental skills to interpret quantitative information, to comprehend statistical analyses, and to analyze data by means of a statistical software.
Main topics of the course:
- Multiple regression and correlation
- Model building with multiple regression
- Logistic regression
The following free software will be used: R and RStudio. The language of the course is English.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Students have to deliver individual assignments and perform a research paper presentation, showing the quantitative skills acquired (e.g., proper use of statistical concepts, use of data, analyze data correctly and interpret the results accordingly). The final grade is based on all the following tasks:
- Active participation (20%)
- Writing assignment I (30%)
- Writing assignment II (30%)
- Presentation (20%)

Important Grading Information:
The provision of all partial tasks is a prerequisite for a positive assessment, if not explicitly noted otherwise.

The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if this is expressly requested by the lecturer (e.g. for individual work tasks).

In order to ensure good scientific practice, the lecturer can provide for a "grading-related discussion" (plausibility check) of the written work submitted, which must be completed successfully.

All students who received a place in the course are assessed if they have not deregistered from the course in due time or if they have not credibly shown an important reason for their failure to deregister after the cause for this reason does no longer apply
Students who credibly show an important reason (e.g. a longer illness) for the withdrawal from a course with continuous assessment are not assessed.
Whether this exception applies is decided by the lecturer. The request for deregistration must be submitted immediately.

If any requirement of the course has been fulfilled by fraudulent means, be it for example by cheating at an exam, plagiarizing parts of a written assignment or by faking signatures on an attendance sheet, the student's participation in the course will be discontinued, the entire course will be graded as "not assessed" and recorded accordingly.
You can find these and other provisions in the study law: https://satzung.univie.ac.at/studienrecht/.

In case you have received three negative assessments of a continuously assessed course and want to register for a fourth attempt, please make sure to contact the StudiesServiceUnit Sociology. (for more information see "third attempt for continuously assessed courses" https://soziologie.univie.ac.at/info/pruefungen/#c56313)

The plagiarism-detection service (Turnitin in Moodle) can be used in course of the grading.

Mindestanforderungen und Beurteilungsmaßstab

Tasks are graded with a scale from 1 (excellent) to 5 (fail). Students have to respect the deadlines agreed for each task. If the student does not perform in one of the tasks according to the deadline, the course is considered failed. To have an overall positive evaluation, students need a grade of at least 4 for each task. Attendance at the sessions is expected, if a student misses more than one session, the course is considered failed.

Prüfungsstoff

Literatur

Statistical Methods for the Social Sciences (5th Edition), Alan Agresti
Other material will be indicated during the course.

Zuordnung im Vorlesungsverzeichnis

in 905: Ausschließlich für das Pflichtmodul MA M Methoden

Letzte Änderung: Mi 13.11.2024 12:06