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Causal inferences with cross-sectional large scale assessment data

Edited by Leslie Rutkowski, University of Oslo, Norway

In this special issue of Large-Scale Assessments in Education, we offer several papers on the topic of causal inferences with international large-scale assessment (ILSA) data. The papers here are primarily empirical analyses of ILSA data that feature different methods, all with an aim toward estimating the effect of some cause. Each paper also includes a brief introduction to the method at hand along with a discussion of the important statistical assumptions that underpin each method and whether or not the assumptions are plausible in the given circumstance. Although the treatment is selective, the methods featured here are commonly used in practice and serve as a useful introduction to specific methods of data analysis applied in a quasi-experimental context. Important to keep in mind is that the implemented methods are applied to observational data, most of which are also cross-sectional. The paper by Rutkowski and Delandshere balances the empirical offerings by taking a critical perspective on drawing causal inferences through the lens of a validity framework.

  1. Research

    Is computer availability at home causally related to reading achievement in grade 4? A longitudinal difference in differences approach to IEA data from 1991 to 2006

    Research on effects of home computer use on children’s development of cognitive abilities and skills has yielded conflicting results, with some studies showing positive effects, others no effects, and yet othe...

    Monica Rosén and Jan-Eric Gustafsson

    Large-scale Assessments in Education 2016 4:5

    Published on: 24 February 2016