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Quantitative Methods in the Social Sciences 2

  • 7.5 credits

The course deals with advanced applications of linear regression (such as interactions, transformations, marginal effects, testing hypothesis, decomposition methods, measurement errors), linear regression extensions (multilevel and panel data models), as well as models for discrete outcomes (binary, ordered and multinomial logit).

In the course, emphasis is placed on the craft, i.e., to implement, understand and interpret analyzes with the aforementioned methods rather than statistical theory, although some statistical theory is included as part of the deeper understanding required by the course. In addition, the course provides an overview of research traditions in quantitative social sciences (such as experiments, quasi experiments, observation data models) as well as the  problems of causality. The course focuses on computer exercises where the course participants themselves work with analyzing a data material.