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Penalized Best Linear Prediction of True Test Scores

Author

Listed:
  • Lili Yao

    (Educational Testing Service)

  • Shelby J. Haberman

    (Edusoft)

  • Mo Zhang

    (Educational Testing Service)

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.

Suggested Citation

  • Lili Yao & Shelby J. Haberman & Mo Zhang, 2019. "Penalized Best Linear Prediction of True Test Scores," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 186-211, March.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:1:d:10.1007_s11336-018-9636-7
    DOI: 10.1007/s11336-018-9636-7
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    References listed on IDEAS

    as
    1. Shelby Haberman & Sandip Sinharay, 2010. "Reporting of Subscores Using Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 209-227, June.
    2. Shelby J. Haberman & Jiahe Qian, 2007. "Linear Prediction of a True Score From a Direct Estimate and Several Derived Estimates," Journal of Educational and Behavioral Statistics, , vol. 32(1), pages 6-23, March.
    3. Shelby J. Haberman, 2008. "When Can Subscores Have Value?," Journal of Educational and Behavioral Statistics, , vol. 33(2), pages 204-229, June.
    4. Shelby J. Haberman & Sandip Sinharay, 2010. "The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring," Journal of Educational and Behavioral Statistics, , vol. 35(5), pages 586-602, October.
    Full references (including those not matched with items on IDEAS)

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