Research Methods (MSB104)

Solving complex tasks and presenting them in a scientific manner is the core of modern business analytics. Solving such tasks requires training, which is at the heart of this course. The course builds on the Data Analytics course and brings its content to a practical application. It is challenged-based and improves the students’ abilities in scientific writing and group work.

Course description for study year 2023-2024. Please note that changes may occur.


Course code




Credits (ECTS)


Semester tution start


Number of semesters


Exam semester


Language of instruction



The subject areas covered are:

  • The foundations of empirical research
  • How to design and structure empirical studies to address a real-world empirical question
  • How to go about collecting data in order to address the empirical question at hand
  • How to solve the question using empirical data and advanced empirical tools
  • How to convincingly present empirical insights

Learning outcome


On completion of the course, students will be able to:

  • Systematically and critically review an existing body of empirical literature 1
  • Formulate relevant research questions
  • Develop an appropriate empirical research design
  • Collect and analyse quantitative data
  • Conduct high-level empirical analysis and adequately present their results
  • Be familiar with ethical perspectives in empirical research and critically evaluate own data management practices


Students will be able to:

  • Digest and evaluate scientific research
  • Use data analytics in empirical problems
  • Conduct an empirical study of high scientific quality
  • Develop and critically assess research hypotheses and questions
  • Demonstrate abilities to scientifically communicate in writing and presentation

Required prerequisite knowledge


Recommended prerequisites

Appropriate bachelor background in core business fields as well as mathematics for business/economic and statistics for business/economics.


Form of assessment Weight Duration Marks Aid
Folder evaluation 1/1 Letter grades

The evaluation may be based on problem sets, group work, individual work.It is not possible to resit the exam.

Coursework requirements

Attendance is necessary in order to participate in some of the components in the assessment.

Course teacher(s)

Course teacher:

Jon-Sander Amland

Course coordinator:

Dora Zsuzsanna Simon

Course teacher:

Hammad Shaikh

Course teacher:

Kwadwo Atta-Owusu

Course teacher:

Hongyan Shi

Course teacher:

Peter Molnar

Study Program Director:

Yuko Onozaka

Method of work

In this course, you will learn through group work, individual study and consultation with the course supervisor.

Expectations: 280 ECTS hours divided between lectures, meetings, independent study, and out-of-class (group) work.

Overlapping courses

Course Reduction (SP)
Research Methods for Business Sciences (MØA103_1) 10
Data Analytics and Research Methods (MØA112_1) 10
Data Analytics and Research Methods (MSB112_1) 10
Research Methods for Business Sciences, Data Analytics and Research Methods ( MØA103_1 MØA112_1 ) 20
Data Analytics and Research Methods, Research Methods for Business Sciences ( MSB112_1 MØA103_1 ) 20
Data Analytics and Research Methods, Data Analytics and Research Methods ( MSB112_1 MØA112_1 ) 30
Data Analytics and Research Methods, Research Methods for Business Sciences, Data Analytics and Research Methods ( MSB112_1 MØA103_1 MØA112_1 ) 30

Open for

Master of Science in Accounting and Auditing Business Administration - Master of Science

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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