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2025/2026  BA-BPOLO2401U  Research Design and Quantitative Methods

English Title
Research Design and Quantitative Methods

Course information

Language English
Course ECTS 15 ECTS
Type Mandatory
Level Bachelor
Duration Two Semesters
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for Global Relations
Course coordinator
  • Mainly: 1st semester
    Benjamin Carl Krag Egerod - Department of International Economics, Goverment and Business (EGB)
  • Mainly: 2nd semester
    Florian Hollenbach - Department of International Economics, Goverment and Business (EGB)
  • Mainly: 1st semester
    Yumi Park - 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 27/05/2025

Relevant links

Learning objectives
At the end of the course, students should be able to:
  • Create and interpret data visualizations in R.
  • Demonstrate critical assessment skills regarding measurement choices for important social, political, and business concepts, such as discrimination, trust, corruption among others.
  • Use R to generate and interpret basic univariate and bivariate statistical data summaries.
  • Estimate and interpret multiple regression models.
  • Use R to estimate and interpret advanced quantitative methods to answer important questions in the social sciences and study of business, in particular estimating causal effects
  • Discuss the fundamentals of statistical inference and carry out null hypothesis testing.
  • Apply bivariate regression analysis and interpret the specific results including coefficients, standard errors, p-values, etc.
  • Understand the importance of and inherent difficulties in estimating causal effects.
  • Understand and discuss structure and components of quality research designs.
  • Evaluate different research design strategies and select adequate quantitative approaches for estimating causal effect for a given question.
  • Summarize and illustrate the differences between experimental and observational studies employed within business and social sciences.
Examination
The exam in the subject consists of four parts:
DP1 - Research Design and Quantitative Methods:
Sub exam weight10%
Examination formHome assignment - written product
Individual or group examIndividual exam
Size of written productMax. 5 pages
Assignment typeWritten assignment
Release of assignmentAn assigned subject is released in class
DurationWritten product to be submitted on specified date and time.
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodAutumn
Make-up exam/re-exam
Same examination form as the ordinary exam
The make-up / retake exam for those who are ill, have failed or not attended the original exam is a new paper offering different questions that will be assigned by staff
Description of the exam procedure

The learning objectives 1-3 are relevant to this exam.

 

Generative AI is an obstacle to learning in this type of course, and we strongly discourage its use. Remember that this first test is aimed at training you for the 4-hour sit in, where GenAI is not available.

DP2 - Research Design and Quantitative Methods:
Sub exam weight40%
Examination formWritten sit-in exam on CBS' computers
Individual or group examIndividual exam
Assignment typeWritten assignment
Duration4 hours
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodWinter
AidsLimited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • An approved calculator. Only the models HP10bll+ or Texas BA ll Plus are allowed (both models are non-programmable, financial calculators).
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
The make-up / retake exam for those who are ill, have failed or not attended the original exam is a new paper offering different questions that will be assigned by staff
Description of the exam procedure

The learning objectives 1-7 are relevant to this exam.

 

This is a 4-hour sit in. There will be not internet connection, and GenAI is not available. Use of GenAI is not allowed.

DP3 - Research Design and Quantitative Methods:
Sub exam weight10%
Examination formHome assignment - written product
Individual or group examIndividual exam
Size of written productMax. 5 pages
Assignment typeWritten assignment
Release of assignmentAn assigned subject is released in class
DurationWritten product to be submitted on specified date and time.
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodSpring
Make-up exam/re-exam
Same examination form as the ordinary exam
The make-up / retake exam for those who are ill, have failed or not attended the original exam is a new paper offering different questions that will be assigned by staff
Description of the exam procedure

The learning objectives 1, 3, 4, 7, and 8 are relevant to this exam.

 

Generative AI is an obstacle to learning in this type of course, and we strongly discourage its use. Remember that GenAI makes mistakes, and you will not be able to spot them, unless you fulfill the learning objectives of the course.

DP4 - Research Design and Quantitative Methods:
Sub exam weight40%
Examination formHome assignment - written product
Individual or group examIndividual exam
Size of written productMax. 10 pages
Assignment typeWritten assignment
Release of assignmentThe Assignment is released in Digital Exam (DE) at exam start
DurationWritten product to be submitted on specified date and time.
Grading scale7-point grading scale
Examiner(s)One internal examiner
Exam periodSummer
Make-up exam/re-exam
Same examination form as the ordinary exam
The make-up / retake exam for those who are ill, have failed or not attended the original exam is a new paper offering different questions that will be assigned by staff
Description of the exam procedure

The learning objectives 1- 11 are relevant to this exam.

 

Generative AI is an obstacle to learning in this type of course, and we strongly discourage its use. Remember that GenAI makes mistakes, and you will not be able to spot them, unless you fulfill the learning objectives of the course.

Course content, structure and pedagogical approach

In the realms of business, public policy, and the non-governmental sector, there is an unprecedented emphasis on the need for "evidence." Consequently, the current labor market exhibits a strong demand for graduates proficient in data analysis, capable of designing inquiries, and adept at drawing conclusions from quantitative data.

 

This course covers both the theoretical background and application of introductory, intermediate, and more advanced statistical and quantitative methods in business and social science. Upon completion of the course, students should have a theoretical understanding of the methods introduced and the ability to apply them to specific research problems. The course will be taught over two semesters. In the first semester, we will discuss concepts related to research design, measurement, introductory data analysis (incl. data visualization and descriptive statistics). We will then transition to multiple regression and the estimation of statistical uncertainty. The second semester starts with a more thorough coverage of multiple regression analysis. Next, the course will introduce more advanced statistical methods and methods of causal inference, for example, difference-in-differences estimation and regression discontinuity designs. The course consists of both lectures and applied exercises, in which we will actively work on implementing the methods covered in lectures. 

 

Please note: This course uses an applied approach and thus we will use software (R) to work with data throughout the whole course and in the examination. Beyond the software examples in the applied textbooks, students will receive a refresher on the software in the early stages of the course.

 

In relation to Nordic Nine

Throughout the course, students will develop important competencies in several of the Nordic Nine capabilities. Understanding and working with data is becoming ever more important in our changing world. In this class, students learn important skills to empirically evaluate causal statements and analyze quantitative data (NN2). Students will practice thinking critically and analytically to answer important societal challenges (NN6). Moreover, students work collaboratively in data analytics tasks, collectively teaching and learning from each other (NN8).

 

Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Methodology
Research-like activities
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
Lectures, exercise classes, feedback session, and supporting video materials. 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
Research Design and Quantitative methods is an applied, exercise-based course, and students will receive feedback continuously during lectures and exercise 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 or during the class meetings. Workshop instructors will walk through the exercise with the students, thereby providing additional group feedback.

Additionally, solutions for the weekly exercises will also be posted. 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) 190 hours
Lectures, class exercises etc. over two semesters 68 hours
Exam (the two end-of-semester exams) 76 hours
Last updated on 27/05/2025