Universität Wien FIND

230089 SE From Description to Inference: Basic Tools to Learn from Data (2021S)

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


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


max. 35 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Donnerstag 04.03. 09:00 - 10:30 Digital
Donnerstag 11.03. 09:00 - 10:30 Digital
Donnerstag 18.03. 09:00 - 10:30 Digital
Donnerstag 25.03. 09:00 - 10:30 Digital
Donnerstag 15.04. 09:00 - 10:30 Digital
Donnerstag 22.04. 09:00 - 10:30 Digital
Donnerstag 29.04. 09:00 - 10:30 Digital
Donnerstag 06.05. 09:00 - 10:30 Digital
Donnerstag 20.05. 09:00 - 10:30 Digital
Donnerstag 27.05. 09:00 - 10:30 Digital
Donnerstag 10.06. 09:00 - 10:30 Digital
Donnerstag 17.06. 09:00 - 10:30 Digital
Donnerstag 24.06. 09:00 - 10:30 Digital


Ziele, Inhalte und Methode der Lehrveranstaltung

Focusing on the practice of empirical research in the social sciences, this course is intended to provide the foundations in descriptive and inferential statistics. The aim of the course is to provide students with fundamental skills to interpret quantitative information, to comprehend statistical reports, and to analyze data by means of a statistical software.
Main topics of the course:
-Statistical Concepts and Descriptive Statistics,
-Inferential Statistics,
-Analyzing association between variables.
The course will be both theoretical and practical. For the applied sessions, the following software will be used: R and RStudio.
The course will be held digitally, the Moodle platform will also be used. The language of the course is English.

Art der Leistungskontrolle und erlaubte Hilfsmittel

In the course of the semester, students have to deliver an individual assignment at the end of each - main - topic, for a total of four assignments. In each assignment, the student has to show the quantitative skills acquired (e.g., proper use of statistical concepts, use of data, analyze data correctly and interpret the results accordingly). For the final grade: the first assignment weights 20%, the second and third assignments weight 25%, the fourth assignment weights 30%.

Important Grading Information:
If not explicitly noted otherwise, all requirements mentioned in the grading scheme must be met.
If a required task is not fulfilled, this will be considered as a discontinuation of the course. In that case, the course will be graded as ‘fail’ (5), unless there is a major and unpredictable reason for not being able to fulfill the task on the student's side (e.g. a longer illness).
In such a case, the student may be de-registered from the course without grading.
Whether this exception applies is decided by the lecturer.
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 will be entered into the electronic exam record as ‘fraudulently obtained’.
The plagiarism-detection service (Turnitin in Moodle) can be used in course of the grading: Details will be announced by the lecturer.

Mindestanforderungen und Beurteilungsmaßstab

Each assignment is graded with a scale from 1 (excellent) to 5 (fail). Students have to deliver each assignment in due time, according to the agreed deadlines, which will be announced during the course. For each 12 hours of delay in submitting the assignment, half a point will be subtracted from the grade. If the student does not deliver all the assignments, the course is considered failed. Attendance is compulsory, up to three absences without notice will be excused. Students need an overall grade of 4 or less to pass the course.



Statistical Methods for the Social Sciences (4th Edition), Agresti & Finlay.
Statistical Methods for the Social Sciences (5th Edition), Agresti
When necessary, other material will be indicated during the course.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 21.04.2021 11:26