This course is an introductory course in quantitative methods available to PhD students. The goal of the
course is to provide the PhD students with basic understanding of the role and potential of quantitative
methods in social science research, basic ability to understand and evaluate the merits and shortcomings of
other researchers’ (quantitative) studies, basic ability to apply certain quantitative techniques in your own
research, and basic orientation that facilitates further self-study or taking more advanced courses on
quantitative methods

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

1. Read and communicate quantitative studies by appropriate statistical terminology
2. Identify which kind of multivariate statistical analysis is appropriate for a specific problem
3. Explain important concepts of statistical methods for analysis of multivariate data

4. Explain the potentials and limitations of statistical methods for analysis of multivariate data
5. Analyze, criticize and document potential weaknesses of the quality of the data and its
consequences
6. Conduct multivariate statistical analyses with an appropriate statistical software
7. Assess the goodness-of-fit of a multivariate model

8. Assess the general usefulness/weaknesses of the statistical analyses treated in the course
9. Recognize the common errors made in multivariate analysis

1) Descriptive statistics + graphical analysis
2) Survey design and design of experiment
3) Explanatory and confirmatory factor analysis
4) Regression analysis
5) Sturctural equation modelling

Lectures, labs and seminar

The teaching is conducted in English.

Admitted to a doctoral program at a recognized business school or university. An expected common
background is 15 credits in introductory level statistics

The course is graded Fail (U) or Pass (G).

Compulsory attendance to labs. Possible grades are Pass/Fail.
• Hand-in reports in connection to each lab (about 4), fulfils ILOs 4-9
• Individual written assigments in connection to each lecture, fulfils ILOs 1-4 and

A course evaluation will be conducted at the end of the course

Hair Jr., J. F., Black, W.C., Babin, B.J. & Anderson, R.E, Multivariate Data Analysis: Pearson New International
Edition, 7 ed., Pearson Education. Latest edition
Byrne, Barbara, Structural Equation Modeling With AMOS: Basic Concepts, Applications, and
Programming, Routledge. Latest edition
A list of articles will be supplied at the course introduction.