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

doi:10.1027/1016-9040.13.3.180

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 Center Linz

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17 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 language	English
Pages (from-to)	180-187
Number of pages	8
Journal	European Psychologist
Volume	13
Issue number	3
DOIs	https://doi.org/10.1027/1016-9040.13.3.180
Publication status	Published - 2008

Keywords

e-Learning
Euclidean distance
Matching
Nonexperimental design
Randomization

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10.1027/1016-9040.13.3.180

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title = "A Euclidean distance-based matching procedure for non randomized comparison studies",

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.",

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AU - Spiel, Christiane

AU - Lapka, Dominik

AU - Gradinger, Petra

AU - Zodlhofer, Eva Maria

AU - Reimann, Ralph

AU - Schober, Barbara

AU - Wagner, Petra

AU - von Eye, Alexander

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - e-Learning

KW - Euclidean distance

KW - Matching

KW - Nonexperimental design

KW - Randomization

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JO - European Psychologist

JF - European Psychologist

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

Abstract

Keywords

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