2024/2025 KAN-CPOLO2050U Advanced Quantitative Methods
English Title | |
Advanced Quantitative Methods |
Course information |
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Language | English |
Course ECTS | 7.5 ECTS |
Type | Mandatory (also offered as elective) |
Level | Full Degree Master |
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/MSc i International Business and Politics,
MSc
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Course coordinator | |
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Main academic disciplines | |
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Teaching methods | |
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Last updated on 25-06-2024 |
Relevant links |
Learning objectives | ||||||
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Course prerequisites | ||||||
While we will recap core concepts, basic knowledge of linear regression, statistical uncertainty and descriptive statistics will be presumed. The course will rely on the statistical software R, but no prior knowledge of the software is assumed. | ||||||
Examination | ||||||
The course shares exams with | ||||||
KAN-CPOLO1043U | ||||||
Course content, structure and pedagogical approach | ||||||
In business, public policy and the non-governmental sector, the demand for “evidence” is stronger than ever. Managers and policy-makers place an increasing premium on knowing “what works”, when they decide on 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 ever have been.
This course provides a case-based introduction to techniques that allow us not only to measure which policies “work”, but also to quantify how much they work. That is, drawing causal inferences. We will cover four broader areas:
1) The so-called potential outcomes framework for thinking about causal inference. 2) Research designs that allow for drawing causal inferences. 3) The statistical implementation in the software R. 4) Substantive interpretation of the results of a statistical analysis.
The course will be taught as a mix of lectures in larger groups and exercise classes in smaller groups. The teaching format is “particular general particular”. We will introduce a particular question, then discuss how such a question is handled in general by reviewing core concepts from the literature, and we then return to the particular application by focusing implementation and extensions.
Throughout the course, we will follow an applied, hands-on approach, emphasizing the implementation and interpretation of statistical analyses. Hence, students will spend a substantial amount of time working with software. The exercise classes will be based on group work prior to class. Solutions and implementations will then be discussed in class. The course software will be R. All activities will be based on data from actual research projects.
In relation to Nordic Nine The course supports the Nordic Nine (NN) capabilities by training analytic skills and critical thinking through hands-on work with data. In the lectures, students will learn fundamental concepts from statistics and causal inference. In the exercises, they will apply these concepts by criticizing a published piece research (NN6), discussing constructively how to improve its research design (NN6), and honing data analytic skills by replicating the results with real-world data (NN2). This will form the basis of class discussions, where we will deliberate about how a particular piece of research informs us about society (NN1), and helps resolve the challenges facing us (NN3). |
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Description of the teaching methods | ||||||
The course will be taught in-person. The teaching in the physical classroom will be supplemented by pre-recorded videos introducing and elaborating on aspects of the syllabus. For example, step-by-step coding introductions will be recorded and made available for the students to revisit as they work on their exercises. | ||||||
Feedback during the teaching period | ||||||
AQM is a workshop-based course, and students will
receive feedback continuously during workshop classes. Throughout
the sessions, personal feedback is provided in-class by means of
the exercise sessions.
In particular, all lectures are followed by exercise sessions. Here, students will be provided with feedback on the assignments they have prepared before the class meets. Workshop instructors will walk through the exercise with the students, thereby providing additional group feedback. Besides this, students are welcome to make use of the instructors’ weekly office hours. |
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Student workload | ||||||
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