Course content

The content of the course varies. It may be given as an overview course, covering several statistical techniques and methods - such as multilevel analysis, panel data analysis or structural equation models - or it may be concentrated on one technique. The course may also link the analytical techniques to advanced rules and aspects of conducting social research, including research designs and data collection/evaluation techniques. Please consult the course coordinator to receive more information about the exact course content of this year.

Learning outcome

Knowledge - the student shall:

• achieve insight and understanding into one or more techniques in applied statistical data analysis.

Skills - the student shall demonstrate the ability to:

• be able to apply the learned techniques in a practical manner (using Stata and/or R).
• demonstrate the ability to use one of the analytical techniques addressed during the course in an independent manner through written work.

Learning methods and activities

Intensive lectures/seminars (full days), normally during one week in the beginning of the semester (often the very first week). The sessions consist of a mix of theory and practice, that is, the presented topics and techniques will be subsequently applied in a practical manner (using Stata and/or R). Supervision of term paper. The paper is to be an independent discussion of a topic taught in lectures. If 6 or fewer students sign up for a planned course during the first 2 teaching weeks, the course will be offered as an instructed reading course.

Recommended previous knowledge

See required previous knowledge.

Required previous knowledge

Master's degree in Sociology/Political Science or equivalent. Knowledge of basic statistical methods such as OLS and logistic regressions.

Course materials

To announced at the beginning of the course.

Credit reductions