| Learning objectives |
- Understand the theoretical assumptions behind statistical
methods covered in the course
- Be able to apply linear regression models and more advanced
methods introduced in the course to problems in the social sciences
and study of business
- Understand and make use of analytical approaches to better
understand human behavior in the political and business world
- Understand the importance of and inherent difficulties in
estimating causal effects
- Ability to use R to estimate and interpret advanced
quantitative methods to answer important questions in the social
sciences and study of business, in particular estimating causal
effects
- Be able to evaluate different research design strategies for
estimating causal effect for a given question
|
| Course prerequisites |
| Students should have taken Quantitative Methods
for Business and Social Science in the IBP Program |
| Examination |
|
Advanced
Topics in Quantitative Methods:
|
| 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 |
| Release of assignment |
The Assignment is released in Digital Exam (DE)
at exam start |
| Duration |
72 hours 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)
|
Description of the exam
procedure
The exam is a take-home based on questions posed by the course
instructor and will include some data analysis. The exam may cotain
any material covered in lectures, readings, and
exercises.
|
|
| Course content, structure and pedagogical
approach |
|
The course covers both the theoretical background and
application of more advanced statistical and quantitative methods
in business and social science. Upon completion of the course,
students should have a theoretical understanding of the methods
introduced and apply them to specific research problems. Building
on the first 'Quantitative Methods for Business and Social
Science' course in IBP, this course will first deepen
students' theoretical understanding of multiple regression
analysis. Next, the course will cover more advanced statistical
methods and methods of causal inference, for example,
difference-in-differences estimation and regression discontinuity
designs. The course consists of both lectures and applied
exercises, in which we will actively work on implementing the
methods covered in lectures.
Please note: This course uses an applied approach and thus we
will use software (R) to work with data throughout the whole course
and in the examination. Beyond the software examples in the applied
textbooks, students will receive a refresher on the software in the
early stages of the course.
In relation to Nordic Nine
Throughout the course, students will develop important
competencies in several of the Nordic Nine capabilities.
Understanding and working with data is becoming ever more important
in our changing world. In this class, students learn important
skills to empirically evaluate causal statements and analyze
quantitative data (NN2). Students will practice thinking critically
and analytically to answer important societal challenges (NN6).
Moreover, students work collaboratively in data analytics tasks,
collectively teaching and learning from each other (NN8).
|
| Description of the teaching methods |
| Lectures, exercise classes. Feedback will be
provided in exercises classes and office hours. |
| Feedback during the teaching period |
| Students will receive in-class feedback on the
mandatory activity, particularly about areas that need further
study and work. Solutions for the mandatory activity will also be
posted and subsequently discussed in detail in class. In addition,
we encourage you to ask questions or make comments in class and
form self-study groups to secure peer feedback on your work. We
will also use online forums to provide further discussion of the
topics discussed in lectures and exercises. We will use office
hours for specific inquiries, although these can never be a
substitute for participation in lectures and classes. |
| Student workload |
| Preparation time (readings and coding for lectures, exercises,
activities) |
190 hours |
| Lectures, class exercises etc. |
38 hours |
| Exam (actual exam period) |
72 hours |
|
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