2 August 2019
Research Designs and Causal Inference
Social scientists are frequently interested in the analysis of change and its causes. For this reason, typical research interests refer not only to the observation of change but first and foremost to the analysis of its causes - the question why change occurs is at the core of many empirical studies in social science research. The extent to what change can be traced back to a specific cause unambiguously - the so-called internal validity - depends on the order and the course of the empirical study, in other words, the research designs. For this reason, decisions concerning the research design are crucial to the success of any causal analysis. Against the background of these considerations, different ways to classify research designs are introduced, and the relations between research questions and designs are introduced and discussed with regard to the strengths and weaknesses of different kinds of research designs.
Prof. Dr. Stefanie Eiflerr is professor of Sociology and Empirical Social Research at the Catholic University of Eichstätt-Ingolstadt, Germany;
Dr. Heinz Leitgöb is research associate at the Catholic University of Eichstätt-Ingolstadt, Germany.
Participants will find the course useful if they:
- are considering to collect experimental or observational data to answer causal research questions and need advanced knowledge about potential research designs and their appropriate implementation;
- want to gain insight into the prerequisites of causal inference from a design perspective.
Participants should have graduate-level knowledge about
- main types of research and sampling designs,
- survey methodology,
- main techniques of cross-sectional and longitudinal data analysis.
By the end of the course participants will:
- have obtained an extensive overview over various types of quantitative research designs;
- be familiar with the concept of causality from different perspectives:
- be able to select the appropriate research design to answer causal research questions.
The course does not cover how to investigate or model causal effects using statistical software!
EUR 120: Student/PhD student rate.
EUR 180: Academic/non-profit rate.
The rates include the tuition fee, course material, access to library and IT facilities and coffee/tea/soft drinks.