040195 KU Data Analysis on Organization and Personnel (MA) (2023W)
Continuous assessment of course work
Labels
service email address: opim.bda@univie.ac.at
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).
- Registration is open from Mo 11.09.2023 09:00 to Fr 22.09.2023 12:00
- Registration is open from Tu 26.09.2023 09:00 to We 27.09.2023 12:00
- Deregistration possible until Fr 20.10.2023 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Tuesday
10.10.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
17.10.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
24.10.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
31.10.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
07.11.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
14.11.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
21.11.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
28.11.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
05.12.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
12.12.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
09.01.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
16.01.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
23.01.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
30.01.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Aims, contents and method of the course
Assessment and permitted materials
The final grade will be based on assignments, group presentation (may be substituted with assignments), and active in class participation. Attendance during the lectures is mandatory.• Assignments (60%)
• Group presentation (30%) (may be substituted with additional assignments)
• Participation (10%).The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).
• Group presentation (30%) (may be substituted with additional assignments)
• Participation (10%).The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).
Minimum requirements and assessment criteria
1 (sehr gut) → 100-89 poins
2 (gut) → 88-76 poins
3 (befriedigend) → 75-63 poins
4 (genügend) → 62-50 poins
5 (nicht genügend) → 49-0 poins
2 (gut) → 88-76 poins
3 (befriedigend) → 75-63 poins
4 (genügend) → 62-50 poins
5 (nicht genügend) → 49-0 poins
Examination topics
Topics discussed in class with focus on application of statistical methods.
Reading list
Hair et al. (2013) Multivariate Data Analysis. Pearson
Wooldridge „Introductory Econometrics“
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
Wooldridge „Introductory Econometrics“
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
Association in the course directory
Last modified: Fr 20.10.2023 14:26
The aim of this course is to provide participants with an understanding of the quantitative research process from hypotheses development to testing the hypotheses with the appropriate statistical methods.Goal: Upon completion of the course, participants should be able to conduct their own study and analyses data sets with a variety of statistical methods. Discussed topics include:• Developing and testing hypotheses
• Introduction to univariate and multivariate methods
• Analysis of variance
• Regression analysisThe emphasis is on empirical applications and the mathematics of econometric will be introduced only as needed.Goal: Upon completion of the course, students will be able to undertake regression analysis and inference on a variety of hypotheses involving cross-sectional and time series data.
This course introduces students to regression tools for analyzing data in economics, finance and related disciplines. Extensions include regression with discrete random variables, instrumental variables regression, quasi-experiments, and regression with time series data. The objective of the course is for the student to learn how to conduct – and how to critique – empirical studies in economics, finance and related fields. Accordingly, the emphasis of the course is on empirical applications. The mathematics of econometrics will be introduced only as needed and will not be a central focus.