210015 UE BAK 4 Quantitative Methods of Empirical Social Research (2020W)
(engl.)
Continuous assessment of course work
Labels
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).
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).
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 07.09.2020 08:00 to Mo 21.09.2020 08:00
- Registration is open from Th 24.09.2020 08:00 to Th 01.10.2020 08:00
- Deregistration possible until Mo 19.10.2020 08:00
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Thursday
08.10.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
15.10.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
22.10.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
29.10.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
05.11.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
12.11.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
19.11.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
26.11.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
03.12.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
10.12.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
17.12.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
07.01.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
14.01.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
21.01.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Thursday
28.01.
08:30 - 10:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Information
Aims, contents and method of the course
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 basic descriptive and inferential statistics, as well as the graphic representation of results. The core focus of this course will be hands-on and practical. Students are expected to attend the 210014 VO lecture component, which will cover theoretical concepts and more abstract ideas.Students will learn the basic “tools” to conduct quantitative data analysis using the programming language R. By the end of the course, students should be able to describe and manipulate a dataset and conduct basic inferential analysis R.Due to Covid-19 regulations, the class will be taught in a hybrid format with online components and reduced class room teaching. Students are expected to show up to 45 minutes of class every two weeks, but exceptions will be made for students who fear that this might expose them to a health risk. The teaching format is subject to ongoing change as new regulations are imposed.
Assessment and permitted materials
The final assessment will be based on the following components:
(1) Participation (10% of final grade) Regular attendance in class (maximum 2 classes can be missed)
(2) Three 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.
(3) A mid-term exam before Christmas (25% of final grade). The test 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. Joint work is NOT allowed for the final assignment. Deadline for handing in the final assignment: 31 March 2021.
(1) Participation (10% of final grade) Regular attendance in class (maximum 2 classes can be missed)
(2) Three 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.
(3) A mid-term exam before Christmas (25% of final grade). The test 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. Joint work is NOT allowed for the final assignment. Deadline for handing in the final assignment: 31 March 2021.
Minimum requirements and assessment criteria
In order to complete the course with a positive grade students have to attempt all seminar parts.Students are allowed to miss two classes. Attendance will be evaluated based on online quizzes.The software turnitin will be used to check plagiarism
Examination topics
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.
Reading list
The following readings are required:
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). New Jersey: Pearson Education International
- 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:
- Paul M. Kellstedt, and Guy D. Whitten. 2018 (3rd edition). The fundamentals of political science research. Cambridge: Cambridge University Press
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). New Jersey: Pearson Education International
- 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:
- Paul M. Kellstedt, and Guy D. Whitten. 2018 (3rd edition). The fundamentals of political science research. Cambridge: Cambridge University Press
Association in the course directory
Last modified: Mo 28.09.2020 16:09