350190 SE MSC.III - Quantitative Research Methods - Abt. D (2024S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 05.02.2024 09:00 bis Mi 21.02.2024 12:00
- Anmeldung von Fr 01.03.2024 09:00 bis Fr 08.03.2024 12:00
- Abmeldung bis So 31.03.2024 12:00
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Donnerstag
07.03.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Donnerstag
21.03.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Donnerstag
18.04.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
N
Donnerstag
02.05.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Donnerstag
16.05.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Donnerstag
13.06.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Donnerstag
27.06.
13:00 - 16:00
ZSU - USZ II, EDV Raum, 2. Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Course assessment will be based on homework, interim tests, and a final test.- Homework (20%)
- Theoretical knowledge (20%)
- Final test (60%, can be repeated)The entire completion of tasks by students must take place by the following April 30th for courses with continuous assessment in the winter semester, and by September 30th at the latest for those in the summer semester. Students who did not sign out from the course are to be assessed. In the case of a negative assessment, an examination before a committee is not permitted; the course must be repeated. Legal source: Statutes of the University of Vienna §10 (4, 5, 6).You are expressly pointed out that if a result was obtained by fraudulent means (e.g. copying, plagiarism, use of unauthorized aids, forgeries, ghostwriting, etc.), the entire PI-LV is considered cheated and counts as a trial. (Entry in U: SPACE: X = not assessed).
- Theoretical knowledge (20%)
- Final test (60%, can be repeated)The entire completion of tasks by students must take place by the following April 30th for courses with continuous assessment in the winter semester, and by September 30th at the latest for those in the summer semester. Students who did not sign out from the course are to be assessed. In the case of a negative assessment, an examination before a committee is not permitted; the course must be repeated. Legal source: Statutes of the University of Vienna §10 (4, 5, 6).You are expressly pointed out that if a result was obtained by fraudulent means (e.g. copying, plagiarism, use of unauthorized aids, forgeries, ghostwriting, etc.), the entire PI-LV is considered cheated and counts as a trial. (Entry in U: SPACE: X = not assessed).
Mindestanforderungen und Beurteilungsmaßstab
Attendance is compulsory (at least 75% of the units).The minimum requirement for a positive assessment is 60% of the total points.Grading:
Nicht genügend (5): <60%
Genügend (4): 60-70%
Befriedigend (3): 71-80%
Gut (2): 81-90%
Sehr gut (1): 91-100%
Nicht genügend (5): <60%
Genügend (4): 60-70%
Befriedigend (3): 71-80%
Gut (2): 81-90%
Sehr gut (1): 91-100%
Prüfungsstoff
Slides and materials as provided in the seminar.SPSS application (Attention: you would need an SPSS Statistics Software license, https://zid.univie.ac.at/en/software-for-students/user-guides/spss-statistics/).
Literatur
Field, A. (2018). Discovering statistics using IBM SPSS statistics, 5. ed., SAGE
O'Donoghue, P. (2012). Statistics for Sport and Exercise Studies: An Introduction, Routledge
Hopkins, W.G. (2013). A new view of statistics. http://www.sportsci.org/resource/stats/
O'Donoghue, P. (2012). Statistics for Sport and Exercise Studies: An Introduction, Routledge
Hopkins, W.G. (2013). A new view of statistics. http://www.sportsci.org/resource/stats/
Zuordnung im Vorlesungsverzeichnis
MSC.III
Letzte Änderung: Di 27.02.2024 11:07
- Confident application of quantitative methods in empirical research
- Competence in interpreting statistical measures in Sport Science-related literature
- Ability to autonomously analyze and present empirical dataContent:
- Use of the SPSS statistical analysis tool
- Statistical methods (descriptive analyses, correlation/regression, and inferential statistics)
- Application of these statistical methods within the research process (study planning, analyses, and interpretation)Methods:
- Autonomous and guided work at the PC