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Lehrveranstaltungen
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Fortgeschrittene Analysemethoden der quantitativen Sozialforschung: Applied Panel Data Analysis (Part A) -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mo, 8:00 - 10:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
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Fortgeschrittene Analysemethoden der quantitativen Sozialforschung: Applied Panel Data Analysis (Part B) -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 2,00 SWS, ECTS: 6
- Termine:
- Mo, 10:00 - 12:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
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Fortgeschrittene Verfahren der Längsschnittanalyse: Applied Panel Data Analysis -
- Dozentinnen/Dozenten:
- Michael Gebel, Sonja Scheuring
- Angaben:
- Seminar, 4,00 SWS, ECTS: 12
- Termine:
- Mo, 8:00 - 12:00, RZ/00.07
- Voraussetzungen / Organisatorisches:
The seminar starts at October 22, 2018.
Participants are expected to be familiar with multiple linear and binary logistic regression analysis. Students are also required to be familiar with the statistics software Stata. These skills could either be acquired or refreshed in self-studies or by attending an additional compact tutorial course (teaching in German) at the beginning of the winter term. Link to Stata tutorial [https://univis.uni-bamberg.de/form?__s=1113&dsc=anew/lecture_view&lvs=sowi/sozwiss/metho/einfhr&anonymous=1&founds=sowi/sozwiss/metho/einfhr&__e=1&sem=2018w&codeset=utf8]
If you like to participate in the compact tutorial course, please register via the virtual campus (VC). Link to VC: https://vc.uni-bamberg.de/moodle/course/view.php?id=31887
It is not necessary to register for the seminar in advance (e.g. via Flexnow, via email, etc.). More information about the course and registration guidelines will only be provided during the first seminar session.
Module-related examination: Portfolio (time: 3 months); could be either written in English or German
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