400003 SE SE Methods for Doctoral Candidates (2015S)

Social scientists increasingly apply the Bayesian approach to diverse kinds of research topics. This development is due to a series of its attractive features: e.g. handling aggregate data without sampling processes, analyzing small N data, estimating models with complex likelihood functions and the easy set up of newly developed statistical models. Furthermore, the increasing capacity of modern computers enables a wider range of researchers to conduct such computationally intensive estimations. Despite of these advantages there is still backlog demand in respect to several points: First, it is not widely enough acknowledged that Bayesian statistics and conventional statistics are based on different views concerning theory and data. Second, the literature, including text books, is in general too technical to motivate most social scientists to apply Bayesian analysis to their own research questions. Third, the programs needed for Bayesian analysis is not user friendly enough for most social scientists.

The course aims to close these gaps. First, the course provides a well-grounded conceptional background for Bayesian analysis. Second, participants will be guided how to read the literature concerning Bayesian statistics and interpret the results. Third, this course gives a practical introduction in a specific software for Bayesian analysis with political science examples. More specifically, the course covers the following topics: Fundamentals of Bayesian analysis, Bayesian estimation using MCMC and estimation of various regression models (binary logit/probit, poisson, multi-level, robust regression etc.) in the Bayesian framework. The course combines lectures and lab sessions. The lecture deals with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, these skills are applied to political and social science data. Hence, course participants also learn the basic knowledge of BUGS which is needed to conduct Bayesian estimation.

Participants are required to have basic knowledge in statistical analysis including regression models with different types of dependent variables. Furthermore, in lab sessions participants learn how to use BUGS in R. Therefore, the basic knowledge in R is also recommended.

For credits participants are required to submit a take-home-exam in April.