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

Cite this

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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.",

keywords = "Common-item IRT equating, MCMC estimation, generalized partial credit model, mixture Rasch model, subgroup equating, subscore equating",

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

note = "Publisher Copyright: Copyright {\textcopyright} 2019 JMASM, Inc.",

year = "2018",

doi = "10.22237/jmasm/1559653467",

language = "English",

volume = "17",

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AU - Choi, Youn Jeng

AU - Cohen, Allan S.

AU - Kim, Seohyun

AU - Lu, Zenqiu

PY - 2018

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AB - 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.

KW - Common-item IRT equating

KW - MCMC estimation

KW - generalized partial credit model

KW - mixture Rasch model

KW - subgroup equating

KW - subscore equating

UR - https://www.scopus.com/pages/publications/85108897770

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DO - 10.22237/jmasm/1559653467

M3 - Article

AN - SCOPUS:85108897770

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JF - Journal of Modern Applied Statistical Methods

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M1 - eP2673

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The Impact of Equating on Detection of Treatment Effects

Abstract

Bibliographical note

Keywords

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Cite this