Penalized Best Linear Prediction of True Test Scores

Lili Yao (), Shelby J. Haberman () and Mo Zhang ()
Additional contact information
Lili Yao: Educational Testing Service
Shelby J. Haberman: Edusoft
Mo Zhang: Educational Testing Service

Psychometrika, 2019, vol. 84, issue 1, No 10, 186-211

Abstract: Abstract In best linear prediction (BLP), a true test score is predicted by observed item scores and by ancillary test data. If the use of BLP rather than a more direct estimate of a true score has disparate impact for different demographic groups, then a fairness issue arises. To improve population invariance but to preserve much of the efficiency of BLP, a modified approach, penalized best linear prediction, is proposed that weights both mean square error of prediction and a quadratic measure of subgroup biases. The proposed methodology is applied to three high-stakes writing assessments.

Keywords: true test score; PBLP; subgroup biases (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-018-9636-7

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