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A Euclidean Distance-Based Matching Procedure for Nonrandomized Comparison Studies
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For intervention programs that are applied in natural settings, randomization often is difficult or impossible to achieve. If treated individuals are compared with individuals from a nonrandomized comparison group, the inference of causality can be biased. Similar distributions in the relevant characteristics of the treatment and the comparison groups cannot be expected. To adjust between-group comparisons for preexisting differences, this article proposes a simple matching procedure. This procedure involves pairing of treatment and comparison individuals based on observable characteristics, using Euclidean distance scores. Application of the proposed Euclidean-distance matching (EuM) procedure to data from the Viennese E-Lecturing (VEL) project yields satisfying results. Possible generalizations and applications of the EuM procedure are discussed.
Title: A Euclidean Distance-Based Matching Procedure for Nonrandomized Comparison Studies
Description:
For intervention programs that are applied in natural settings, randomization often is difficult or impossible to achieve.
If treated individuals are compared with individuals from a nonrandomized comparison group, the inference of causality can be biased.
Similar distributions in the relevant characteristics of the treatment and the comparison groups cannot be expected.
To adjust between-group comparisons for preexisting differences, this article proposes a simple matching procedure.
This procedure involves pairing of treatment and comparison individuals based on observable characteristics, using Euclidean distance scores.
Application of the proposed Euclidean-distance matching (EuM) procedure to data from the Viennese E-Lecturing (VEL) project yields satisfying results.
Possible generalizations and applications of the EuM procedure are discussed.
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