Universität Wien

210015 UE BAK 4 Quantitative Methods of Empirical Social Research (2021W)

(engl.)

6.00 ECTS (2.00 SWS), SPL 21 - Politikwissenschaft
Continuous assessment of course work
MIXED

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Moodle

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).

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 06.10. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 13.10. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 20.10. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 27.10. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 03.11. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 10.11. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 17.11. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 24.11. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 01.12. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 15.12. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 12.01. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 19.01. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre
  • Wednesday 26.01. 15:00 - 16:30 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
    Hybride Lehre

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 (2021W)” 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.
Students who wish to complete the course online are free to do so, but I will strive to create opportunities for in-presence trouble-shooting.

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) Four 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.

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.
The software turnitin will be used to check plagiarism

90-100 = 1. Excellent
80-89 = 2. Good
70-79 = 3. Satisfactory
60-69 = 4. Sufficient
< 60 = 5. Fail

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.

The final paper is due on February 16th 2022

Reading list

The following readings are required - please consider buying:
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). New Jersey: Pearson Education International

Association in the course directory

Last modified: Fr 12.05.2023 00:19