2021/2022 BA-BSOCO1024U Quantitative Methods II
English Title | |
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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Course coordinator | |
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Teaching methods | |
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Last updated on 03-12-2021 |
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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Examination | ||||||||||||||||||||||||
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||
The course introduces students to quantitative methods at an intermediary level, and includes introductions to regression analysis for continuous and categorical variables.
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 assignments, and are expected to make (at least) two presentations in class.
The aim of this course is to provide the students with both theoretical and practical knowledge about quantitative methods such as multivariate OLS, logit regression, and identification strategies for causal inference at an intermediate and advanced level, enabling the student to expand and develop the knowledge and skills achieved in the RDQM course.
Students learn to understand fundamental principles behind the statistical tools introduced in the course and to apply these to a specific research problem. |
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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 exercise questions and task for students. During class, students will get feedback on their work on the questions/tasks from teachers, 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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Student workload | ||||||||||||||||||||||||
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