The Impact of Equating on Detection of Treatment Effects

Youn Jeng Choi; Allan S. Cohen; Seohyun Kim; Zenqiu Lu

doi:10.22237/jmasm/1559653467

The Impact of Equating on Detection of Treatment Effects

Youn Jeng Choi, Allan S. Cohen, Seohyun Kim, Zenqiu Lu

Department of Education

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	eP2673
Pages (from-to)	2-20
Number of pages	19
Journal	Journal of Modern Applied Statistical Methods
Volume	17
Issue number	2
DOIs	https://doi.org/10.22237/jmasm/1559653467
State	Published - 2018

Bibliographical note

Publisher Copyright:
Copyright © 2019 JMASM, Inc.

Keywords

Common-item IRT equating
MCMC estimation
generalized partial credit model
mixture Rasch model
subgroup equating
subscore equating

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10.22237/jmasm/1559653467

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title = "The Impact of Equating on Detection of Treatment Effects",

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.",

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author = "Choi, {Youn Jeng} and Cohen, {Allan S.} and Seohyun Kim and Zenqiu Lu",

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KW - mixture Rasch model

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KW - subscore equating

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The Impact of Equating on Detection of Treatment Effects

Abstract

Bibliographical note

Keywords

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