230150 UE M6 Data analysis using Stata (2024W)
Sociological Specialisation of Choice
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Di 27.08.2024 00:01 bis Di 17.09.2024 23:59
- Anmeldung von Mo 23.09.2024 00:01 bis Do 26.09.2024 23:59
- Abmeldung bis So 20.10.2024 23:59
Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 08.10. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 15.10. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 22.10. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 29.10. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 05.11. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 12.11. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 19.11. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- N Dienstag 26.11. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 03.12. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 10.12. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 17.12. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 07.01. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 14.01. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 21.01. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
- Dienstag 28.01. 13:15 - 14:45 Class Room 3 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-25
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
The course introduces the Stata programming language and software environment, tailored for beginners and intermediate users. It covers data exploration, data wrangling, descriptive data analysis and visualization. The aim is to equip and support participants with the skills to use Stata for data analysis, including preparing, visualizing and modeling data, as well as creating reproducible syntax-based codes and research reports. Emphasis is placed on practical application, implementation and the development of problem-solving skills.A basic understanding of quantitative social research methods and statistics, equivalent to a Bachelor's level in Sociology, is expected. Prior knowledge of Stata is not required, but participants should be willing to engage and practice the program-based working method. An interest in quantitative statistical methods of empirical social research is important.
Art der Leistungskontrolle und erlaubte Hilfsmittel
Participation in this course requires the ability to use Stata. During the lab seminars, the software will be available on the PCs in the classroom. For completing the weekly exercises, students can use the PCs in the computer room on the ground floor of the New Institute Building (NIG). Alternatively, students can obtain the software at a reduced price from the Central IT Service: https://zid.univie.ac.at/software-fuer-studierende/The course follows a learning by doing course: while the lab seminars offer a detailed introduction to various topics with many explanations and examples, weekly homework assignments are given to practice data analysis using Stata. By the end of the seminar, students are also expected to write a research report that applies the data analysis skills they have acquired.All written contributions must be submitted through Moodle and will be checked using the plagiarism detection software.-----
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)
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)
Mindestanforderungen und Beurteilungsmaßstab
The final grade will be based on the submission of
- the weekly exercises (70%) and
- the final research report on a chosen topic (30%), following the course leader’s instructions.For a positive assessment of the course, all partial achievements must be fulfilled.
- the weekly exercises (70%) and
- the final research report on a chosen topic (30%), following the course leader’s instructions.For a positive assessment of the course, all partial achievements must be fulfilled.
Prüfungsstoff
Literatur
Kohler, Ulrich & Kreuter, Frauke (2012): Data analysis using Stata. Stata Press. (Multiple editions)
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 20.09.2024 12:06