Courses Multivariate Data Analysis

3613 Multivariate Data Analysis , 8 sp

Intermediate 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, Discriminant Analysis, LOGIT, and Cluster Analysis. The statistical program SPSS is used in the course.
The course is built on the concept of cooperative learning in small teams. You will get a joint grade for the overall performance of your team.
Students with Statistics as their minor should include this course in their bachelor's studies. This course is also recommended for students with other quantitative subject as their major.
The course Multivariate Data Analysis (5 ECTS, course code 3677) 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.
The two courses are taught together, with a smaller number of lectures (and analyses) for course 3677.

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 were multivariate analysis 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

214 hours divided into
scheduled work: 44 h
non-scheduled work: 170 h
Contact hours: Lectures, exercises & computer labs (44 h).Self-study: Preparing for the weekly lectures (reading about the concepts covered during the lectures, analysing examples etc), 50 h.
                Article reviews (preparation and reporting), 40 h.
                Assignment (data analysing and reporting), 40 h.
                Final exam (preparation, writing and evaluation), 40 h.


Final exam (open book) 40%
Article reviews 30%
Computer assignments and activity points 30%

  • Hair, J. F., Black, W.C, Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis: a global perspective. 7th ed. or earlier. Upper Saddle River (N.J.): Prentice Hall.

Selected chapters as specified by the instructor.

Student who have taken the 5 ECTS version of MDA (course code 3677) cannot take this 8 ECTS version.

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

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