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2021/2022  BA-BPOLO2010U  Quantitative Methods for Business and Social Science

English Title
Quantitative Methods for Business and Social Science

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Bachelor
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, BSc
Course coordinator
  • Zoltan Fazekas - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 21-05-2021

Relevant links
Learning objectives
  • Identify and select adequate quantitative approaches to analyze different social research problems.
  • Summarize and illustrate the differences between experimental and observational studies employed within business and social sciences.
  • Demonstrate critical assessment skills regarding measurement choices for important social, political, and business concepts, such as discrimination, trust, corruption among others.
  • Identify and evaluate the assumptions behind the statistical methods introduced in the course.
  • Discuss the fundamentals of statistical inference and carry out null hypothesis testing.
  • Apply multiple regression analysis to social science and business datasets (country and individual level) and interpret the specific results including coefficients, standard errors, p-values, etc.
  • Be able to use statistical software (R) to generate results that are used as empirical supporting information to meet the preceding objectives and offer substantive interpretation of software output.
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 1
Compulsory home assignments
The mandatory assignment is an individual 5 page written product on specific statistical analysis tasks analogous to the content of exercises. Approved/not approved will be granted based on the submitted written product. The assignment will be handed in around midway of the course.

The mandatory assignment must be approved for the student to participate in the final exam. Feedback on the assignment will be offered in class and during office hours (see below for more details).

If the mandatory assignment is not approved or there has been documented illness a second assignment will be offered before the ordinary exam takes place. This assignment will be an individual 5 page 72-hour take home assignment on set of statistical analysis tasks.

Please note that to submit the retake mandatory assignments it is a precondition that the student has made a valid attempt in the set activities, unless it can be documented that the lack of submission/participation was caused by illness or similar circumstances.

More information on prerequisites for participating in the exam: compulsory activities can be found in the BSc IBP Programme Regulations §13.1-5.
Examination
Quantitative Methods for Business and Social Science:
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 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)
Course content, structure and pedagogical approach

The course provides students with theoretical and applied knowledge about statistical and quantitative methods in business and social science at an introductory and intermediate level. On completion of the course, the student should be able to understand the methods introduced in the course and apply them to specific research problems. The course introduces students to causal inference, basic statistical analysis, and multiple regression analysis. The course consists of a mix of lectures and exercises. Throughout the course we will follow an applied, hands-on approach relying on software implementation in parallel of the theoretical work.

 

Blended learning: A short video walk-through of the software solutions will be made available prior to some exercises. Students can review those and compare with their own solutions. In such a manner, the exercises can focus on interpretation, discussion, and additional extensions.

 

Please note: This course uses an applied approach and thus we rely throughout the whole course and in the examination on software use (R). Beyond the software examples in the applied textbook, students will receive a thorough introduction to the software in the early stages of the course, early stage exercise supporting materials, and have the possibility to work on parallel homework tasks.
Description of the teaching methods
Lectures, exercise classes, feedback session, and supporting video materials
Feedback during the teaching period
We will offer in-class feedback in a dedicated format for the mandatory activity, with prior collection of focal points and queries from students, emphasising areas that need further study and work. Solutions for the mandatory activity will also be posted in a video format. For the voluntary, group-based, parallel homework tasks we will also dedicate a feedback session at the end of the course, focusing on exam preparation and the milestones achieved. 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 further offer answers to questions regarding lecture and exercise content and also office hours for specific inquires, although these can never be a substitute for participation in lectures and classes.
Student workload
Preparation time (readings and coding for lectures, exercises, activities) 160 hours
Lectures, class exercises etc. 44 hours
Exam (actual exam period) 48 hours
Expected literature

Kosuke, Imai (2017/2018). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
Last updated on 21-05-2021