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210015 UE BAK3 Quantitative methods (2025S)
Continuous assessment of course work
Labels
Details
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- N Thursday 06.03. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 13.03. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 20.03. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 27.03. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 03.04. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 10.04. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 08.05. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 15.05. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 22.05. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 05.06. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 12.06. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Thursday 26.06. 11:30 - 13:00 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
Information
Aims, contents and method of the course
This course is complementary to the course "VO BAK 3 Quantitative Methoden”.This course aims to equip students with the basic applied skills needed to understand and carry out basic data analysis. The content of the course includes basic descriptive and inferential statistics.The aim of the course is to equip students with the basic applied skills needed to carry out easy data projects on their own. The content of the course includes basic descriptive and inferential statistics, as well as the graphic representation of results. The course design relies on practical exercises in the computer lab and interactive discussions. Students will revise the basics of empirical quantitative research methods and learn to apply the basic tools of quantitative data analysis using the open-source software R (with RStudio). It is recommended to install the necessary software (R, RStudio) on your own laptop before the start of the course. Both are available online free of charge.By the end of the course, students should be able to describe and manipulate a dataset and conduct basic inferential analyses. Students should also be able to develop and answer research questions using quantitative methods and appropriately interpret and present quantitative research findings.Please note that the course will be instructed in English. This requires that class discussions, weekly assignments, written tests, and the final paper are completed in English.
Assessment and permitted materials
The final assessment will be based on the following components:
(1) Participation (10 points) Regular attendance in class (a maximum of 2 classes can be missed) and active participation in class discussions.
(2) Five homework assignments (25 points): Assignments will be based on materials from the course texts.
(3) A mid-term exam (25 points): The exam will cover theoretical questions about quantitative methods of empirical social research and the interpretation of R outputs. Duration: 45 minutes.
(4) Final assignment (40 points): At the end of the course, students will be required to write a final paper (2,000–2,500 words) that formulates and answers a research question using quantitative methods.Students must submit their assignments in PDF format along with the corresponding scripts. Turnitin will be used to check for plagiarism.
(1) Participation (10 points) Regular attendance in class (a maximum of 2 classes can be missed) and active participation in class discussions.
(2) Five homework assignments (25 points): Assignments will be based on materials from the course texts.
(3) A mid-term exam (25 points): The exam will cover theoretical questions about quantitative methods of empirical social research and the interpretation of R outputs. Duration: 45 minutes.
(4) Final assignment (40 points): At the end of the course, students will be required to write a final paper (2,000–2,500 words) that formulates and answers a research question using quantitative methods.Students must submit their assignments in PDF format along with the corresponding scripts. Turnitin will be used to check for plagiarism.
Minimum requirements and assessment criteria
Attendance Policy:
Attendance in the first session of the course is compulsory. Students who miss the first session will be deregistered from the course. During the semester, students are allowed to miss up to two classes.Prerequisites:
A basic understanding of empirical research and statistics is assumed. Therefore, students are expected to have already completed or be currently enrolled in the course "VO BAK 3 Quantitative Methoden."Minimum Requirements:
In order to complete the course with a positive grade, attendance is mandatory (only two classes can be missed), and all assignments must be submitted.Grading Scale:
- 100-87 Points Excellent (1)
- 86-75 Points Good (2)
- 74-63 Points Satisfactory (3)
- 62-50 Points Sufficient (4)
- 49-0 Points Insufficient (5)
Attendance in the first session of the course is compulsory. Students who miss the first session will be deregistered from the course. During the semester, students are allowed to miss up to two classes.Prerequisites:
A basic understanding of empirical research and statistics is assumed. Therefore, students are expected to have already completed or be currently enrolled in the course "VO BAK 3 Quantitative Methoden."Minimum Requirements:
In order to complete the course with a positive grade, attendance is mandatory (only two classes can be missed), and all assignments must be submitted.Grading Scale:
- 100-87 Points Excellent (1)
- 86-75 Points Good (2)
- 74-63 Points Satisfactory (3)
- 62-50 Points Sufficient (4)
- 49-0 Points Insufficient (5)
Examination topics
The examination will focus on various statistical concepts covered in class and will include basic data analysis using the software R. Detailed instructions for the homework assignments and the final assignment will be posted on Moodle in due course.
Reading list
Recommended:
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). Pearson Education International.
- Elena Llaudet & Kosuke Imai (2022). Data Analysis for Social Science. Princeton University Press.
Further readings will be announced in the course.
- Alan Agresti (2018). Statistical methods for the social sciences (5th edition). Pearson Education International.
- Elena Llaudet & Kosuke Imai (2022). Data Analysis for Social Science. Princeton University Press.
Further readings will be announced in the course.
Association in the course directory
Last modified: Fr 10.01.2025 00:02