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2020/2021  BA-BIBAO2021U  Research Methods II: Statistics

English Title
Research Methods II: Statistics

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Bachelor
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc in Business, Asian Language and Culture
Course coordinator
  • Benjamin Carl Krag Egerod - Department of International Economics, Goverment and Business (EGB)
  • Toshimitsu Ueta - Department of International Economics, Goverment and Business (EGB)
This course is taught and coordinated by Toshimitsu Ueta and Benjamin Carl Krag Egerod.
Main academic disciplines
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 15-12-2020

Relevant links

Learning objectives
  • Identify and select appropriate quantitative approaches to analyze different research problems.
  • Explain the fundamental problem of causal inference, and how various quantitative approaches introduced in the course address it.
  • Summarize and illustrate the differences between experimental and observational studies as related to drawing causal inferences.
  • Identify and evaluate the causal assumptions behind the techniques introduced in the course.
  • Apply quantitative approaches to empirical data and interpret the specific results (e.g., coefficients, standard errors, p-values.in regression analysis).
  • Conduct data preparation and statistical analyses using statistical software (Stata).
Research Methods II: Statistics:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Duration 7 days to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
A new exam assignment must be answered. This applies to all students (failed, ill, or otherwise).
Course content, structure and pedagogical approach

In both business and public policy, the demand for “evidence” is stronger than ever. Managers and policymakers place an increasing premium on knowing “what works”, when they decide in which direction to take the company or the country. Additionally, recent decades have seen an explosion of data availability. Combined with advances in causal inference, this puts social scientists in a better position to answer the demand for evidence than they have ever been.

This course provides an applied introduction to statistical techniques that allow us not only to examine whether a strategy or policy “works”, but also to quantify how much they work. This is done through theoretical and applied knowledge about causal inference and statistical methods in business and social science at an introductory and intermediate level.

Upon completion of the course, the student should be able to understand the methods introduced in the course and apply them to a specific research problem. The course introduces students to quantitative approaches for drawing causal inferences, including the experimental design and various quasi-experimental methods. Multiple regression analysis will be the workhorse model. The course consists of a mix of lectures and exercises. Throughout the course we will follow an applied, hands-on approach.

Description of the teaching methods
Lectures and exercise classes are conducted in a mix of online and on campus teaching.
Feedback during the teaching period
We encourage students to ask questions or make comments in class. Also, we highly recommend to form self-study groups to secure peer feedback on your work. We will use online forums to further offer answers to questions regarding lecture and exercise content. Feedback regarding specific inquiries will be offered during ‘office hours’ offered by full-time staff members, although these can never be a substitute for participation in lectures and classes.
Student workload
Lectures 24 hours
Exercises (2 groups) 12 hours
Preparation for class (reading, exercises etc.) 110 hours
Exam 60 hours
Expected literature

Angrist, Joshua D., and Jörn-Steffen Pischke (2014). Mastering'metrics: The path from cause to effect . Princeton University Press.

Last updated on 15-12-2020