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.
- Euclidean distance
- Nonexperimental design