Causal Inference and Comparative Analysis with Large-Scale Assessment Data

Authored by: P. Robinson Joseph

Handbook of International Large-Scale Assessment

Print publication date:  November  2013
Online publication date:  November  2013

Print ISBN: 9781439895122
eBook ISBN: 9781439895146
Adobe ISBN:

10.1201/b16061-26

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Abstract

Education researchers are increasingly interested in making causal inferences rather than simply describing correlations among variables. Drawing causal inferences from data hinges upon the design of the study, with experimental designs facilitating causal inferences most readily. However, oftentimes experimental designs are infeasible or impractical; moreover, researchers may want to make use of the vast amounts of publicly available large-scale secondary datasets. This chapter discusses the assumptions required for making causal inferences. Then, I provide an introduction to several techniques that may facilitate quasi-experimental designs when applied to secondary data, and in the process I note the specific sets of assumptions for inferring causality when using each technique. I conclude with a discussion of special considerations for researchers using international datasets, and I include examples where causal inferences may be possible. In cases where causal inferences are not possible, I discuss how researchers can use the techniques described in this chapter for careful comparative analyses.

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