The Impact of Equating on Detection of Treatment Effects

Authors

Youn-Jeng Choi, University of AlabamaFollow
Seohyun Kim, University of GeorgiaFollow
Allan S. Cohen, University of GeorgiaFollow
Zhenqiu Lu, University of GeorgiaFollow

Abstract

Equating makes it possible to compare performances on different forms of a test. Three different equating methods (baseline selection, subgroup, and subscore equating) using common-item item response theory equating were examined for their impact on detection of treatment effects in multilevel models.

DOI

10.22237/jmasm/1559653467

Erratum

In a previous version of this article, the third author's affiliation was incorrectly given as University of Alabama. This has been corrected to University of Georgia.

Recommended Citation

Choi, Y.-J., Kim, S., Cohen, A. S., & Lu, Z. (2018). The impact of equating on detection of treatment effects. Journal of Modern Applied Statistical Methods, 17(2), eP2673. doi: 10.22237/jmasm/1559653467