Syllabus
Title
2194 Empirical Research and Analysis II
Instructors
Dipl.-Ing.Mag. Anita Zednik, Ph.D.
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
11/22/17 to 11/24/17
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day | Date | Time | Room |
---|---|---|---|
Monday | 12/04/17 | 10:00 AM - 12:30 PM | TC.5.16 |
Thursday | 12/07/17 | 10:00 AM - 12:30 PM | TC.4.17 |
Monday | 12/11/17 | 10:00 AM - 12:30 PM | D5.1.003 |
Thursday | 12/14/17 | 10:00 AM - 12:30 PM | TC.5.12 |
Monday | 12/18/17 | 10:00 AM - 12:30 PM | D5.1.003 |
Thursday | 12/21/17 | 10:00 AM - 12:30 PM | TC.4.17 |
Monday | 01/08/18 | 10:00 AM - 12:30 PM | D5.1.003 |
Thursday | 01/11/18 | 10:00 AM - 12:30 PM | TC.4.17 |
Monday | 01/15/18 | 10:00 AM - 12:30 PM | D5.1.003 |
Thursday | 01/18/18 | 10:00 AM - 12:30 PM | TC.4.17 |
Monday | 01/22/18 | 10:00 AM - 12:00 PM | TC.3.11 |
Data analysis is the basis of any evidence-based managerial decision-making. Data analysis is about recognizing patterns in data so that inferences about the real world can be made. The course teaches students about selected methods of data creation, collection, and analysis. It draws on econometrics and statistical methods developed to estimate economic relationships, testing theoretical hypotheses and evaluating policies.
In particular, this course will cover the methods of laboratory and field experiments, specific approaches to establish causation such as instrumental variables and regression discontinuities, as well as time series data analysis and issues of big data analysis.
On successful completion of the course, you should:
- understand the concept of evidence-based decision-making.
- be able to choose the right method of statistical data analysis to answer a research question;
- have a good understanding of the discussed methods as well as their limitations;
- understand the difference between causality and correlation;
- be able to present and discuss findings from your research; - perform simple analysis with STATA;
The data course is centered on specific problem-based example and case studies. Each broader topic/method is organized in 2 classes. Typically, in the first class we will start with examples for related business/strategy-related research questions, and discuss how to find and/or generate data to answer these questions. This is followed by an introduction of the respective analysis method. In in-class tasks and homework assignments, students are asked to try out data generation and analysis themselves, with data provided to them. The second meeting then usually discusses specific analysis concepts related to the case, and includes practical work with STATA as well as more examples for applications of the method.
Last edited: 2017-06-23
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