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Sabina Haveric
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Angelika Ruf
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Short Course B: Research Designs and Causal Inference

Prof. Dr. Stefanie Eifler, Dr. Heinz Leitgöb

Date: 01.08 - 02.08.2019 ics-file

Location: Cologne

About the lecturer - Prof. Dr. Stefanie Eifler

About the lecturer - Dr. Heinz Leitgöb

Course description

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.
For a full length syllabus of this course, please click here.


Target group

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;
  • they want to gain insight into the prerequisites of causal inference from a design perspective.

Learning objectives

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!


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.

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