2020/2021
BA-BIBAO2021U Research Methods II: Statistics
| English Title |
| Research Methods II:
Statistics |
|
|
| 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 |
|
|
|
Last updated on
15-12-2020
|
| 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).
|
| Examination |
|
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
Permalink