2022/2023  BA-BSOCO1024U  Quantitative Methods II

 English Title Quantitative Methods II

Course information

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
Course coordinator
• Florian Hollenbach - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
• Methodology and philosophy of science
• Statistics and quantitative methods
Teaching methods
• Face-to-face teaching
Last updated on 01-07-2022

Relevant links

Learning objectives
Upon completion of the course, the student should be able to
• Formulate and operationalize research problems for which one or more of the methods introduced in the course is suitable
• Select the appropriate methods to best identify possible causal effects in a given research question
• After identifying the appropriate approach, conduct quantitative, empirical analysis using R and correctly interpret the results
• Discuss and account for the underlying principles behind the applied method(s), and reflect on their strengths and weaknesses
• Discuss the implications of the analytical results, possible modifications, and develop recommendations for potential policymakers, program directors, and organizations
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).
Examination
 Quantitative Methods II: 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 48 hours to prepare Grading scale 7-point grading scale Examiner(s) One internal examiner Exam period Spring Make-up exam/re-exam Same examination form as the ordinary exam Description of the exam procedure The exam is a take-home exam based on a question posed by the course instructor.
Course content, structure and pedagogical approach

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.

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 undertake an impact evaluation to identify the causal effect of a policy or other treatment. The group work will help students to practice critical thinking and collaborating constructively. Results will be presented by one or two of the groups and discussed together in the exercise class.

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 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.

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 exepcted to complete the assignment in groups prior to the exercise classes. One or two groups will present their solutions. The results will be discussed by all 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 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.
Student workload
 Lectures 20 hours Exercises 18 hours Preparation of lectures (2 h per 2h lecture) 40 hours Preparation of exercises (2 h per 2h exercise) 36 hours Prep workshop: Getting started with R 8 hours Exam preparation 64 hours Exam 20 hours
Last updated on 01-07-2022