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2024/2025 BA-BSOCO1024U Quantitative Methods II
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| Quantitative Methods II |
Course information |
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| Language | English |
| Course ECTS | 7.5 ECTS |
| Type | Mandatory |
| Level | Bachelor |
| Duration | One Quarter |
| Start time of the course | Third Quarter |
| Timetable | Course schedule will be posted at calendar.cbs.dk |
| Study board |
Study Board for BSc in Business Administration and
Sociology
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| Teaching methods | |
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| Last updated on 01-07-2024 | |
Relevant links |
| Learning objectives | ||||||||||||||||||||||||
Upon completion of the course, the student should
be able to
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| Course prerequisites | ||||||||||||||||||||||||
| Students are presumed to be familiar with basic
descriptive and inferential statistics, and with concepts such as
statistical significance, p-values, confidence intervals,
correlation, and the role of control variables introduced in RDQM.
The course is also related to the issues covered in RDQM (e.g. research design, sampling, and variable measurement as well as the role of quantitative data in mixed methods designs, strengths and weaknesses of using quantitative data). |
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| Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||||
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Number of compulsory
activities which must be approved (see section 13 of the Programme
Regulations): 1
Compulsory home
assignments
The compulsory assignment is a group-based (3-4 students, random assignment) written product (maximum 5 pages) on specific statistical analysis and their interpretation, similar to and in preparation for the exam. The submitted product will be assessed on a approved / not approved basis. The compulsory assignment must be approved for the student to participate in the final exam. Feedback on the assignment will be offered in exercises and during office hours. If the compulsory assignment is not approved or there has been documented illness a second assignment will be offered before the ordinary exam takes place. The retake will be similar to the initial compulsory assignment. For students’ to be eligible for the retake compulsory assignment, they must have made a valid attempt in the first compulsory assignment or be able document that the lack of submission was caused by illness or similar circumstances. |
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| Course content, structure and pedagogical approach | ||||||||||||||||||||||||
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The course introduces students to quantitative methods at an intermediate level. An initial focus of the course is to introduce students to the theoretical concepts behind causal inference. Next, the course focuses on how to design research to identify the causal effects of policy choices as well as impact / program evaluations (both public and private). The course includes a more advanced treatment of regression analysis and introductions to more advanced research methods. Students will learn how to design research and analyze data to better make evidence based decisions (NN2 & NN3).
The course consists of a mix of lectures and applied statistical analysis and exercises in lab sessions. Students are expected to participate actively during lectures and exercises. For the exercises, students will be given short assignments, similar to case studies. In these assignments, the students will work in groups to apply one of the covered methods and estimate the causal effect of a policy or other treatment. The group work will help students to practice critical thinking and collaborating constructively (NN6). In the exercise classes, students are expected to discuss their approaches to each problem. We will then go over the solution together. This will allow students to receive feedback on their own work, while also seeing the correct solution.
The aim of this course is to provide students with both theoretical and practical knowledge about quantitative methods such as multivariate OLS, panel data methods, and other identification strategies for causal inference at an intermediate and advanced level. The course will enable the student to be analytical with data (NN2) and further develop the knowledge and skills achieved in the RDQM course.
Students will learn to understand the fundamental principles behind each of the statistical tools covered in the course and will be able to apply these to specific research problems.
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| Description of the teaching methods | ||||||||||||||||||||||||
| Lectures and class work | ||||||||||||||||||||||||
| Feedback during the teaching period | ||||||||||||||||||||||||
| Students will get feedback on their work during
exercise classes. The exercise classes will revolve around group
work on exercise questions. Students are expected to complete the
assignment in groups prior to the exercise classes. In the exercise
class, students are expected to share their solutions to some of
the problems. The results will be discussed together in the
exercise class.
During the exercise class, students will get feedback on the proposed solutions. Students will get feedback on their work on the questions/tasks from teachers and fellow students, and there will be opportunities to discuss the use and application of methods along with the corresponding analyses. Teachers will give oral feedback based on student answers to exercise questions. |
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