Zobraziť v EDS

Standard Error Estimation for Subpopulation Non-invariance.

Uložené v:
Podrobná bibliografia
Názov: Standard Error Estimation for Subpopulation Non-invariance.
Autori: Jewsbury, Paul A.1 (AUTHOR) pjewsbury@ets.org
Zdroj: Applied Psychological Measurement. Nov2025, Vol. 49 Issue 8, p477-505. 29p.
Predmety: *STATISTICAL errors, *EMPIRICAL research, *MATHEMATICAL statistics, MEASUREMENT errors, TEST scoring
Abstrakt: Score linking is widely used to place scores from different assessments, or the same assessment under different conditions, onto a common scale. A central concern is whether the linking function is invariant across subpopulations, as violations may threaten fairness. However, evaluating subpopulation differences in linked scores is challenging because linking error is not independent of sampling and measurement error when the same data are used to estimate the linking function and to compare score distributions. We show that common approaches involving neglecting linking error or treating it as independent substantially overestimate the standard errors of subpopulation differences. We introduce new methods that account for linking error dependencies. Simulation results demonstrate the accuracy of the proposed methods, and a practical example with real data illustrates how improved standard error estimation enhances power for detecting subpopulation non-invariance. [ABSTRACT FROM AUTHOR]
Copyright of Applied Psychological Measurement is the property of Sage Publications Inc. 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.)
Databáza: Business Source Index
Popis
Abstrakt:Score linking is widely used to place scores from different assessments, or the same assessment under different conditions, onto a common scale. A central concern is whether the linking function is invariant across subpopulations, as violations may threaten fairness. However, evaluating subpopulation differences in linked scores is challenging because linking error is not independent of sampling and measurement error when the same data are used to estimate the linking function and to compare score distributions. We show that common approaches involving neglecting linking error or treating it as independent substantially overestimate the standard errors of subpopulation differences. We introduce new methods that account for linking error dependencies. Simulation results demonstrate the accuracy of the proposed methods, and a practical example with real data illustrates how improved standard error estimation enhances power for detecting subpopulation non-invariance. [ABSTRACT FROM AUTHOR]
ISSN:01466216
DOI:10.1177/01466216251351947