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Nonequivalent Groups with Covariates Design Using Propensity Scores for Kernel Equating

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Quantitative Psychology (IMPS 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 196))

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Abstract

In test score equating, the nonequivalent groups with covariates (NEC) design use covariates with high correlation to the test scores as a substitute for an anchor test when the latter is lacking. However, as the number of covariates increases, the number of observations for each covariate combination decreases. We suggest to use propensity scores instead, which we include in the kernel equating framework using both post-stratification and chained equating. The two approaches are illustrated with data from a large-scale assessment, and the results show an increased precision in comparison with the equivalent groups design and great similarities in comparison with the results when using an anchor test.

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Acknowledgment

The research in this paper was funded by the Swedish Research Council grant: 2014-578.

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Correspondence to Gabriel Wallin .

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Appendix

Appendix

Abbreviations of the Data Designs

ANCHOR-NEC-PS:

Post-stratification under the nonequivalent groups with covariates design using propensity scores

CE NEAT:

Chained equating under the nonequivalent groups with anchor test design

CE-NEC-PS:

Chained equating under the nonequivalent groups with covariates design using propensity scores

EG:

Equivalent groups design

PSE NEAT:

Post-stratification equating under the nonequivalent groups with anchor test design

PSE-NEC-PS:

Post-stratification equating under the nonequivalent groups with covariates design using propensity scores

PSE-NEC-RAW-COV:

Post-stratification equating under the nonequivalent groups with covariates design

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Wallin, G., Wiberg, M. (2017). Nonequivalent Groups with Covariates Design Using Propensity Scores for Kernel Equating. In: van der Ark, L.A., Wiberg, M., Culpepper, S.A., Douglas, J.A., Wang, WC. (eds) Quantitative Psychology. IMPS 2016. Springer Proceedings in Mathematics & Statistics, vol 196. Springer, Cham. https://doi.org/10.1007/978-3-319-56294-0_27

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