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Kernel Equating Using Propensity Scores for Nonequivalent Groups
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0002-7930-6701
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0001-5549-8262
2019 (English)In: Journal of educational and behavioral statistics, ISSN 1076-9986, E-ISSN 1935-1054Article in journal (Refereed) Epub ahead of print
Abstract [en]

When equating two test forms, the equated scores will be biased if the test groups differ in ability. To adjust for the ability imbalance between nonequivalent groups, a set of common items is often used. When no common items are available, it has been suggested to use covariates correlated with the test scores instead. In this article, we reduce the covariates to a propensity score and equate the test forms with respect to this score. The propensity score is incorporated within the kernel equating framework using poststratification and chained equating. The methods are evaluated using real college admissions test data and through a simulation study. The results show that propensity scores give an increased equating precision in comparison with the equivalent groups design and a smaller mean squared error than by using the covariates directly. Practical implications are also discussed.

Place, publisher, year, edition, pages
Sage Publications, 2019.
Keywords [en]
kernel equating, background variables, nonequivalent groups, NEC design, propensity scores
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-157869DOI: 10.3102/1076998619838226OAI: oai:DiVA.org:umu-157869DiVA, id: diva2:1302389
Funder
Swedish Research Council, 2014-578Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-04-08

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Wallin, GabrielWiberg, Marie

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