300251 Soziologische Methoden - quantitativ: Causal Inferences (S) (SoSe 2024)

In applied research of the social sciences, we typically acknowledge that statistical correlations in observational data (in contrast to experimental data) do not necessarily indicate cause-and-effect relationships. However, knowledge of such causal effects is central to the development of theory and the practical relevance of our research. Only when research identifies causal directions of effects between, for instance poverty and health, with sufficient certainty, we are able to design suitable measures to reduce either poverty or to improve health.

Building on knowledge of statistics up to regression analysis, the seminar deals with methods of causal inference in cross-sectional data. These include, in particular, matching methods (e.g. propensity score matching), the instrumental variable method (IV regression) and regression discontinuity design (RDD). The seminar provides an applied introduction to these methods using published papers to illustrate their implementation. Moreover, in hands-on sessions, we will implement the methods using Stata on example data. In addition to the applied training and application of advanced regression methods, the aim is to deepen existing knowledge of alternative research designs in sociology ranging between classical experiments, natural experiments, longitudinal, and cross-sectional data.

Literaturangaben

Brady, Henry E. 2008. Causation and Explanation in Social Science. In: Box-Steffensmeier, Janet M., Brady, Henry E. und David Collier (Hrsg.). The Oxford Handbook of Political Methodology. Oxford: Oxford University Press, 217-270.

Markus Gangl. 2010. Causal Inference in Sociological Research. Annual Review of Sociology 36: 21-47.