A Euclidean distance-based matching procedure for non randomized comparison studies

Christiane Spiel, Dominik Lapka, Petra Gradinger, Eva Maria Zodlhofer, Ralph Reimann, Barbara Schober, Petra Wagner, Alexander von Eye

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)180-187
Number of pages8
JournalEuropean Psychologist
Volume13
Issue number3
DOIs
Publication statusPublished - 2008

Keywords

  • e-Learning
  • Euclidean distance
  • Matching
  • Nonexperimental design
  • Randomization

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