Quantitative Methods

The course gives an introduction to fundamental concepts in statistical analysis, with a focus on statistical inference, where we draw conclusions about populations from sample data. The course is practical, which means that students will be introduced to different statistical methods and datasets that we analyze using these methods. The course introduces students to a quantitative software called R. It is an open-source software with an extensive library of statistical functions. Students will get familiarized with this software through computer labs which run in conjunction with the lectures. The grade from the course will be based on a written term paper. The term paper may be based on data that are supplied by the course administrators or students may choose to use their own data. To increase the potential value of this course, students are encouraged to make the term paper methodically and thematically relevant to their master thesis.

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

• has an fundamental understanding of methodological principles, core concepts and techniques in quantitative research
• has theoretical as well as practical understanding of fundamental concepts of statistical analysis

Skills

• can discuss, apply and assess quantitative methods in relation to a chosen research design
• can collect and analyse own data
• can critically assess secondary data sources

General competence

• is able to use the statistical software R
• can apply relevant theories and concepts in discussions of research methodology
• can independently plan a project's research design and apply appropriate research methods

Up to 20 hours lectures and computer labs.

It is expected that compulsory assignments and examinations must be completed in English. Exceptions may be granted on a case by case basis in consultation with the lecturer responsible for the course.