Applied multivariate analysis

February 21-22, 2018, April 4-5, 2018 and individual supervision (to be agreed upon in each case).

ECTS credits: 7.5
Level of course: Ph.D. course
Course type: Elective
Study location: Bodø
Course coordinator: Tommy Høyvarde Clausen
Teaching language: English
Teaching semester: Spring 2018
Costs: No tuition fees. Costs for semester registration and course literature apply
Course evaluation: Evaluation using final survey.

Course description
The course will give students knowledge and skills regarding multivariate analyses techniques appropriate for analyzing cross sectional data. The techniques include multivariate regression analysis, logistic regression analysis and exploratory factor analysis. The course will have a clear focus on how to apply different analyses methods in order to test theoretical derived hypotheses. Students will be trained in using different techniques through practice sessions.    

Learning outcomes:
After completing the course the students will be able to:

Knowledge – the student can

  • Understand the links among theoretical framework, research questions, hypotheses, research models, multivariate analyses techniques and interpretations of results.
  • Critically consider the appropriateness of different multivariate techniques for testing theoretical derived hypotheses    

Skills – the student can    

  • Articulate appropriate (quantitative) research questions and formulate hypotheses
  • Design research models with hypotheses that can be tested using multivariate techniques
  • Make necessary preparations in order to use multivariate analyses
  • Perform exploratory factor analysis, multiple regression analyses and logistic regression analysis
  • Perform analyses investigating mediating and moderating relationships    

General competence – the student can

  • Reflect on and consider what are appropriate quantitative techniques for testing hypotheses
  • Communicate the results of multivariate analyzes in writing and orally
  • Participate in discussions and debates with regard to multivariate analyzes techniques at academic workshops and academic conferences
  • Understand and critically assess empirical research using applied multivariate analysis (for cross-sectional data), such as academic journal articles.    

Must fulfill the requirements for admission to the PhD program

Mode of delivery:

Learning activities and teaching methods:
Lectures, practice sessions,

Paper, Research paper using (quantitative) multivariate analyses, grades passed or non-passed.

Work requirements: two written/practical assignments in connection to each of the two meetings. Compulsory attendance, lectures and practice sessions.

Course literature and recommended reading
Hair, J.F., Black, W.C, Babin, B.J. & Anderson R.E. (2010) Multivariate Data Analysis 7th ed. Upper Saddle River, New Jersey: Person Prentice Hall.

Collection of articles will be provided

The reading list is subject to amendments at semester start.