• Formulate and operationalise a research problem for which one or more of the methods introduced in the course is suitable
• Select methods that are appropriate for analyzing particular types of variables
• Account for the underlying principles behind the applied method(s), and reflect on their strengths and weaknesses
• Apply the method(s) and interpret the results appropriately in a manner that is relevant to the research problem
• Conduct a quantitative, empirical analysis using a statistical software package.

The course introduces students to quantitative methods at an intermediary level, and includes introductions to regression analysis for continuous and categorical variables. The course consists of a mix of lectures and applied statistical analysis and exercises in lab sessions. Students are expected to participate actively during lectures and exercises. For the exercises, students will be given assignments, and are expected to make (at least) two presentations in class.

The aim of this course is to provide the students with both theoretical and practical knowledge about quantitative methods such as multivariate OLS, logit regression, and identification strategies for causal inference at an intermediate and advanced level, enabling the student to expand and develop the knowledge and skills achieved in the RDQM course. Students learn to understand fundamental principles behind the statistical tools introduced in the course and to apply these to a specific research problem.