Syllabus

Title
1560 Empirical Research and Analysis I
Instructors
Dr. Nickolas Gagnon
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/17/20 to 09/20/20
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Thursday 10/01/20 10:00 AM - 01:00 PM TC.0.01 ERSTE
Thursday 10/08/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 10/15/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 10/22/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 10/29/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 11/05/20 10:00 AM - 01:00 PM Online-Einheit
Thursday 11/12/20 10:00 AM - 01:00 PM Online-Einheit

Procedure for the course when limited activity on campus

If Corona virus restrictions are still in place and class size is larger that allowed room capacity, the course will be conducted in a mixed format with live and online lectures, assignments and exercises.

Contents

Data analysis is the basis of any evidence-based managerial decision making. This 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 theories about real-world behaviour, and evaluating business and public policies.

In particular, this course will provide a review of basic statistical tools, introduce students to regression analysis including linear regression with multiple regressors, non-linear regression models, dummy variables, and longitudinal or panel data methods. The course will be application based and will also cover survey and questionnaire design.

A number of academic research papers will also be discussed to illustrate the main concepts.

To link the statistical and econometric concepts to practical examples, students will be shown how to manage and analyse data during class using the statistical software package STATA.

Learning outcomes

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;

Attendance requirements

In order to learn the content properly, students need to be present in each lecture. 

Many relevant concepts, examples and applications will be discussed which may not be included on the lecture slides.

Teaching/learning method(s)

After a short introduction and recap, the course will focus on specific problem-based examples and case studies. Each broader topic/method is organised 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.

Since all empirical applications will involve the use of the statistical software STATA, you are strongly encouraged to use STATA. 

STATA is freely available on university lab computers, or alternatively you can purchase a single-user student license.

Assessment

Participation (10%)

Problem sets (20%)

Empirical survey study (20%)

Final exam (50%)

Last edited: 2020-07-01



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