LFU:online

Students are able to independently develop and implement a research design to answer a scientific question. The research design includes both data collection and data analysis. Students learn to apply valid and replicable research. 

This course focuses on questions of statistical modeling: which methods allow us to analyze causal relationships on the basis of quantitative data? How can we interpret data and analysis results? We discuss multivariate regression analysis and OLS models, but also other methods (e.g., ‘logistic regression’), modeling of nonlinear relationships, and different types of data. Besides survey data, social sciences also increasingly draw on ‘experiments’ but also quantitative text analyses (e.g., ‘Text as Data’).
Besides teaching the theoretical basics, this course introduces students to the open source statistical software ‘R Studio’. The focus is on individual work (‘Lab Sessions’) and practical application of learned skills in the context of research papers.

This block course is based on the teaching of theoretical and practical basics mainly in an 'asynchronous' format (recorded lectures followed by discussions and practical exercises in online presence via Big Blue Button). For students this means on the one hand independent work from home (reading, video lectures, exercises), and on the other hand active participation and application of the acquired knowledge in the course sessions.

The grade is composed of the following parts, each of which must be completed successfully in order to pass: attendance and active participation in the course sessions (10%), research concept incl. presentation and discussion (20%), final research paper (50%), exercises and short exam (20%).

Imai, K. (2017). An Introduction to Quantitative Social Science.
Tausendpfund, M. (2018). Quantitative Methoden in der Politikwissenschaft.
Wolf, C. und Best, H. (2010). Handbuch der sozialwissenschaftlichen Datenanalyse

successful completion of compulsory modules 1 - 3

This course is based on the statistical software ‘R Studio’. To be able to work with it, please install in advance the software package ‘R’ (https://cran.rstudio.com) and the user interface ‘R Studio’ (https://rstudio.com/products/rstudio/download) for your OS. The software is available online and free of charge.