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230122 UE M4 Quantitative Methods: Cross-sectional Data Analysis (2025S)

5.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Continuous assessment of course work
Mo 03.03. 11:30-12:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)

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. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 17.03. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 31.03. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 28.04. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 12.05. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 26.05. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 16.06. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)
  • Monday 30.06. 11:30 - 14:45 Class Room 2 ZID UniCampus Hof 7 Eingang 7.1, 1.OG ( 2H-O1-13)

Information

Aims, contents and method of the course

This course introduces students to statistical methods for analyzing cross-sectional data, with a focus on applying multivariate regression techniques to sociological questions.

The primary aim is to provide participants with a solid, application-oriented understanding of basic analytical approaches (such as hypothesis testing, linear regression analysis, as well as an introduction to causal diagrams and concepts of mediation and moderation). In addition to statistical basics, the course emphasizes practical applications. Participants will learn to formulate specific research questions or hypotheses, test them using contemporary datasets and appropriate analysis methods, and accurately interpret the results. By the end of the course, students will be able to select the most suitable multivariate method for a given research question. They will independently and confidently use linear regression models, correctly interpret the results, and report findings clearly and accurately by using tables and figures.
Participants will engage in practical exercises and work on a research project in groups. Practical exercises and examples will be done in STATA (or alternatively in R). A basic introduction to STATA will be part of the course.

Assessment and permitted materials


Assessment is based on active participation in the sessions, shorter homework assignments, the presentation of a research concept and giving peer feedback, and the submission of a final research report.

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Important Grading Information:
All students who received a place in the course are assessed if they have not deregistered from the course in due time or if they have not credibly shown an important reason for their failure to deregister after the cause for this reason does no longer apply
Students who credibly show an important reason (e.g. a longer illness) for the withdrawal from a course with continuous assessment are not assessed.
Whether this exception applies is decided by the lecturer. The request for deregistration must be submitted immediately.
For a positive assessment of the course, all partial achievements must be fulfilled.
The plagiarism-detection service (Turnitin in Moodle) can be used in course of the grading.
The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if this is expressly requested by the lecturer (e.g. for individual work tasks).
In order to ensure good scientific practice, the lecturer can provide for a "grading-related discussion" of the written work submitted, which must be completed successfully.
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 recorded accordingly.
You can find these and other provisions in the study law: https://satzung.univie.ac.at/studienrecht/.
In case you have received three negative assessments of a continuously assessed course and want to register for a fourth attempt, please make sure to contact the StudiesServiceUnit Sociology during the registration period (for more information see "third attempt for continuously assessed courses" https://soziologie.univie.ac.at/info/pruefungen/#c56313)

Minimum requirements and assessment criteria

Attendance is compulsory, students may miss a maximum of 2 units = one block unit.

The partial achievements contribute to the final grade as follows:

- Active participation (10%)
- Submission of shorter homework assignments (40%)
- Presentation of a research concept and peer feedback (30%)
- Writing a final report (20%)

The grading scale for the course is as follows:
1 (very good) 100-90%
2 (good) 89-81%
3 (satisfactory) 80-71%
4 (sufficient) 70-60%
5 (unsatisfactory) 59-0%

All partial achievements must be completed successfully (min 60%) in order to pass the course.

Examination topics

Reading list

Recommended literature:
Agresti, Alan (2018): Statistical Methods for the Social Sciences, 5th edition (online available through u:search)
Treiman, Donald J. (2009). Quantitative data analysis: doing social research to test ideas. San Francisco: Wiley, http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470380039.html
Kohler, Ulrich & Kreuter, Frauke (2012): Data analysis using Stata. Stata Press. (Multiple editions)

Additional resources will be shared during the course.

Association in the course directory

Last modified: We 22.01.2025 16:07