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Causal Inference in Educational Research: Approaches, Assumptions and Limitations

  • Stefanie Berger
  • , Svenja Hammer
  • , Stefan Hartmann
  • , Cora Joachim
  • , Thomas Lösch

Research output: Working paperWorking papers

Abstract

The Working Paper gives an overview about the topic of causal inference, covered in the Institute on Statistical Analysis for Education Policy organized by the American Educational Research Association in spring 2013. Because randomized experiments are often difficult to implement into large-scale studies in educational research, inference on the causality of different treatments (e.g. different teaching approaches) is limited. This paper discusses the possibilities to draw causal inferences in non-randomized experiments, and provides an introduction to different analytical approaches, namely propensity score analysis, sensitivity analysis and the regression discontinuity approach. The main idea behind each procedure is explained, advantages and limitations are discussed.
Original languageEnglish
Place of PublicationMainz
PublisherJohannes Gutenberg-Universität Mainz
Number of pages17
Publication statusPublished - 2013

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education

Research areas and keywords

  • Educational science
  • Casual inference
  • propensity score
  • Sensitivity analysis
  • regression discontinuity

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