Courses Multivariate Data Analysis

3677 Multivariate Data Analysis , 5 sp

Advanced studies
Teaching language
Course description

The course is an introduction to multivariate analysis (i.e. statistical techniques that simultaneously analyse multiple measurements on individuals or objects). The course covers techniques such as MANOVA, Principal Component Analysis, Factor Analysis, and Cluster Analysis. The statistical program SPSS is used in the course.

This course is included in the study plan for master's studies in Marketing, Logistics and Management, and can be included as a methods course in master's or doctoral studies in any other subjects than statistics.

Students with Statistics as their minor should take the course Multivariate Data Analysis (8 ECTS, course code 3613) in their bachelor's studies. That course is also recommended for students with a quantitative interest with other subjects as their major.

Learning Goal

You are familiar with the features of multivariate data, can explain the logic behind multivariate analysis and have a working knowledge of the multivariate techniques.

After completing the course, you will be able to
  • examine and prepare data for multivariate analysis
  • apply multivariate methods to well-defined research questions and can carry out multivariate data analysis using SPSS
  • interpret and utilize the analysis results and present the results in written reports
  • dissect and evaluate research reports where multivariate analyses are applied
International Learning Experience

International examples and cases are used, and international articles are reviewed from a statistical perspectiv.



The Hanken course 7777 FUM or 7778 Statistisk analys, or similar skills in hypothesis testing, ANOVA and regression analysis recommended


Lectures & computer labs.

Total Student Workload

Total: 134h
Scheduled (contact hours): 26h
Non-scheduled: 108h

Contact hours: Lectures, exercises & computer labs (26 h).
Self-study: Preparing for the weekly lectures (reading about the concepts covered during the lectures, analysing examples etc), 24 h.
Article reviews and computer assignments (preparation, data analysis and reporting), 64 h.
Final exam (preparation, writing and evaluation), 20 h.


Final exam (open book) 40%
Article reviews and computer assignments 60%

Additional individual assignment for doctoral students.

  • Hair, J. F., Black, W.C, Babin, B. J. & Anderson, R. E. (2018). Multivariate data analysis: (a global perspective (subtitle in earlier versions)). 8th ed. or earlier. Upper Saddle River (N.J.): Prentice Hall.
    Selected chapters as specified by the instructor.

Student who have completed the 8 ECTS version of this course (code 3613) cannot take this 5 ECTS version.

Non-degree studies (Open University, JOO and Contract Studies)

Open university quota: 3
Quota for JOO-students: 3