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An Exploration of Comparability Issues in Educational Research : = Scale Linking, Equating, and Propensity Score Weighting.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Exploration of Comparability Issues in Educational Research :/
其他題名:	Scale Linking, Equating, and Propensity Score Weighting.
作者:	Wu, Tong.
面頁冊數:	1 online resource (152 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-10, Section: A.
Contained By:	Dissertations Abstracts International84-10A.
標題:	Educational tests & measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30423810click for full text (PQDT)
ISBN:	9798379437534

An Exploration of Comparability Issues in Educational Research : = Scale Linking, Equating, and Propensity Score Weighting.
Wu, Tong.

An Exploration of Comparability Issues in Educational Research :Scale Linking, Equating, and Propensity Score Weighting. - 1 online resource (152 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: A.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2023.

Includes bibliographical references

This three-article dissertation aims to address three methodological challenges to ensure comparability in educational research, including scale linking, test equating, and propensity score (PS) weighting. The first study intends to improve test scale comparability by evaluating the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on item response theory (IRT) scale linking accuracy when missing data occur within common items. The relative performance of these six missing data treatment methods under two missing mechanisms is explored with simulated data. Results show that RF, MI, and FIML produce fewer errors for conducting scale linking, whereas LWD is associated with the most errors regardless of testing conditions. The second study aims to ensure test score comparability by proposing a new equating method to account for rater errors in rater-mediated assessments. Specifically, the performance of using an IRT observed-score equating method with a hierarchical rater model (HRM) is investigated under various conditions. The newly proposed equating method leads to comparable bias, SE, and RMSE to that of a traditional IRT observedscore equating method with the use of generalized partial credit model (GPCM) as normal raters scoring the new test forms. However, when aberrant raters are involved in the scoring process, the HRM IRT observed-score equating method generally produces more accurate results in bias and RMSE, though generates comparable SEs to the traditional method. The third study examines the performance of six covariate balance diagnostics when using PS weighting method with multilevel data. Specifically, a set of simulated conditions is used to examine the ability of within-cluster and pooled absolute standardized bias (ASB), variance ratio (VR), and percent bias reduction (PBR) methods to identify a correct PS model. In addition, the association between the balance statistics and the bias in treatment effect is explored. Within-cluster ASB and PBR are observed to be associated with the most accurate results in the choice of PS model as compared to other diagnostics. Pooled ASB is found to have the highest association with the treatment effect bias. By advancing the methodology for addressing comparability issues, the dissertation intends to enhance the validity and improve the quality of educational research.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379437534Subjects--Topical Terms:

3168483
Educational tests & measurements.
Subjects--Index Terms:

Covariate balance diagnosticsIndex Terms--Genre/Form:

542853
Electronic books.

An Exploration of Comparability Issues in Educational Research : = Scale Linking, Equating, and Propensity Score Weighting.
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500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: A.
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504 $a Includes bibliographical references
520 $a This three-article dissertation aims to address three methodological challenges to ensure comparability in educational research, including scale linking, test equating, and propensity score (PS) weighting. The first study intends to improve test scale comparability by evaluating the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on item response theory (IRT) scale linking accuracy when missing data occur within common items. The relative performance of these six missing data treatment methods under two missing mechanisms is explored with simulated data. Results show that RF, MI, and FIML produce fewer errors for conducting scale linking, whereas LWD is associated with the most errors regardless of testing conditions. The second study aims to ensure test score comparability by proposing a new equating method to account for rater errors in rater-mediated assessments. Specifically, the performance of using an IRT observed-score equating method with a hierarchical rater model (HRM) is investigated under various conditions. The newly proposed equating method leads to comparable bias, SE, and RMSE to that of a traditional IRT observedscore equating method with the use of generalized partial credit model (GPCM) as normal raters scoring the new test forms. However, when aberrant raters are involved in the scoring process, the HRM IRT observed-score equating method generally produces more accurate results in bias and RMSE, though generates comparable SEs to the traditional method. The third study examines the performance of six covariate balance diagnostics when using PS weighting method with multilevel data. Specifically, a set of simulated conditions is used to examine the ability of within-cluster and pooled absolute standardized bias (ASB), variance ratio (VR), and percent bias reduction (PBR) methods to identify a correct PS model. In addition, the association between the balance statistics and the bias in treatment effect is explored. Within-cluster ASB and PBR are observed to be associated with the most accurate results in the choice of PS model as compared to other diagnostics. Pooled ASB is found to have the highest association with the treatment effect bias. By advancing the methodology for addressing comparability issues, the dissertation intends to enhance the validity and improve the quality of educational research.
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653 $a Rater-mediated assessments
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