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This is the study programme for 2019/2020. It is subject to change.


MØA103: Research Methods for Business and Social Sciences and MØA104: Data Analytics for Business Decisions is a sequence of methods courses mandatory for first semester master students from all specializations (strategic marketing, innovation, finance, and economics). The former (MØA103) focuses on the scientific process, research design, and introduces the statistical software R and SPSS, whereas the latter (MØA104) focuses on qualitative and quantitative analytical tools relevant for business students.

Learning outcome

Knowledge
On completion of the course, students will have an understanding of the scientific process involved in conducting relevant research for business decisions, including how to:
  • Formulate relevant research questions related to business challenges
  • Systematically and critically review an existing body of empirical literature
  • Develop a relevant conceptual framework
  • Select an appropriate research design
  • Address relevant ethical considerations
  • Determine appropriate sampling
  • Negotiate access to data sources
  • Collect qualitative and quantitative data
  • Handling the software R

Skills
Students will be able to:
  • Consume existing business-relevant research
  • Commission new research studies
  • Design and implement original knowledge discover projects
  • Develop, test, and validate qualitative and quantitative data collection instruments
  • Collect qualitative and quantitative data
  • Import, export, clean, and visualize data in R

General competence
This course will contribute to students' general competence in
  • Conducting scientific research
  • Searching and reviewing relevant literature
  • Develop and critically assess research hypotheses and questions
  • Presenting and engaging in scientific discussion
  • Group work
  • Business decision-making

Contents

Addressing the challenges faced in business and making smart business decisions requires both general and specialized knowledge, which comes from a variety of sources linked to multiple business-related research areas. The modern business manager must be able to interpret scientific findings from existing research, commission new studies on behalf of the firm, and implement original knowledge discovery projects. Moreover, mankind has entered the age of (big) data. This increasingly requires of managers to understand, handle, and utilize new and frequently massive information. To address this challenge, the course introduces students to the statistical software R and SPSS through lectures, assignments and guided (group) learning sessions.

This course provides a comprehensive, master-level introduction to the scientific process available for conducting research relevant for business decisions. The course addresses 1) the foundations of empirical research (including ethical considerations), 2) how to design and structure an empirical study to address a real-world business challenge, 3) how to go about collecting data to address the business challenge at hand, 4) software skills necessary for (quantitative) empirical research. Among the topics covered in this course, include the pros and cons of collecting and using primary versus secondary data, qualitative vs quantitative methodologies, research ethics, how digital technologies are creating new opportunities for business research, and software skills. The course provides students with the skills needed for reviewing an existing body of knowledge, carrying out their own business research projects, writing a master thesis within a business-related specialization field, as well as cleaning, structuring, handling, and visualizing data.

Required prerequisite knowledge

None.

Recommended previous knowledge

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

 

Warning for the Unserious: You are seeking a highly sought-after and competitive MSC degree in business administration from the UiS Business School. Acceptance into the MSC program does not guarantee graduation and diploma. The faculty of the UiS Business School expects that you are highly motivated, possess excellent study habits, behave respectfully of fellow-students and course instructors, and generally show full dedication to your study program.

Exam

Portfolio and Written exam
Weight Duration Marks Aid
Portfolio45/100 A - F
Written exam55/1004 hoursA - F1)
The final grade is based on a portfolio of mandatory work components, including group assignments, a series of graded quizzes, and a comprehensive final examination.
The re-sit examination will be an individual work (weight 45 %) and a written exam (weight 55 %).
1) Course material

Coursework requirements

Attendance

Course teacher(s)

Course coordinator
Gorm Kipperberg , Aslaug Mikkelsen

Method of work

Students are expected to attend, be prepared, and actively engage in lectures, learning team work, and practical sessions. The learning teams comprise 4-8 pre-assigned students each. The learning teams will work together on regular assignments and larger projects. A typical week will look as follows:
  1. Monday: Doing research lecture (3 hours): The instructor presents the core materials and introduce assignments/projects
  2. Wednesday: Doing research practice (3 hours): Students work on practical exercises or group work. This session may also be used for introducing and practicing software.
  3. Friday (2 hours): Students work on their group projects with the supervisor being around for questions.


The expected workload for each 10 ECTS course is approximately 280 hours in total, distributed as follows: i) lectures (30 hours), ii) student seminars & group work (120 hours), iii) independent study of course material (130 hours).

Open to

Business Administration - Master of Science

Course assessment

Student evaluation will be carried out in accordance with the UiS Business School's evaluation system.

Literature

Saunders, M., Lewis, P and Thornhill, A. 2016. “Research Methods for Business Students (7th Edition)” Pearson Education Limited.

Wickham, H. (2017) R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, O'Reilly Media; Edition: 1


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 26.06.2019