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Linking Error on Achievement Levels Accounting for Dependencies and Complex Sampling.

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Title: Linking Error on Achievement Levels Accounting for Dependencies and Complex Sampling.
Authors: Jewsbury, Paul A.1 (AUTHOR)
Source: Journal of Educational Measurement. Spring2026, Vol. 63 Issue 1, p1-29. 29p.
Subject Terms: *Outcome assessment (Education), Measurement errors, Cluster sampling, Minimum variance estimation, Estimation theory, Statistical sampling
Abstract: Alternate assessments of the same construct or assessments that have undergone a change in the conditions of measurement are often linked in an attempt to establish score comparability. As the link must be estimated from the data, linking contributes error variance into estimators. We propose a novel method to account for linking variance in standard error estimation for achievement or proficiency levels, a primary outcome for many international, national, and U.S. state assessments. Achievement levels are proportions of a population within some range of ability, such as the proportion of the population classified as proficient or advanced. The method is validated in a simulation and with real data. Our method allows for sampling weights and complex sampling and involves an easily calculated correction term that may be added to conventional estimates of the error variance, correcting the conventional estimates for neglecting variance due to linking. Furthermore, the method accounts for dependencies between linking with other sources of variance, allowing for the method to be much more widely applicable to a range of score comparisons than traditional methods of linking variance estimation. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: *<searchLink fieldCode="DE" term="%22Outcome+assessment+%28Education%29%22">Outcome assessment (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+sampling%22">Cluster sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Minimum+variance+estimation%22">Minimum variance estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink>
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  Data: Alternate assessments of the same construct or assessments that have undergone a change in the conditions of measurement are often linked in an attempt to establish score comparability. As the link must be estimated from the data, linking contributes error variance into estimators. We propose a novel method to account for linking variance in standard error estimation for achievement or proficiency levels, a primary outcome for many international, national, and U.S. state assessments. Achievement levels are proportions of a population within some range of ability, such as the proportion of the population classified as proficient or advanced. The method is validated in a simulation and with real data. Our method allows for sampling weights and complex sampling and involves an easily calculated correction term that may be added to conventional estimates of the error variance, correcting the conventional estimates for neglecting variance due to linking. Furthermore, the method accounts for dependencies between linking with other sources of variance, allowing for the method to be much more widely applicable to a range of score comparisons than traditional methods of linking variance estimation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jedm.12439
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        Text: English
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      – SubjectFull: Outcome assessment (Education)
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      – SubjectFull: Measurement errors
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      – SubjectFull: Cluster sampling
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      – SubjectFull: Minimum variance estimation
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      – SubjectFull: Estimation theory
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      – SubjectFull: Statistical sampling
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      – TitleFull: Linking Error on Achievement Levels Accounting for Dependencies and Complex Sampling.
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              Text: Spring2026
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