Improving Accuracy and Stability of Aggregate Student Growth Measures Using Empirical Best Linear Prediction

J. R. Lockwood, Katherine E. Castellano and Daniel F. McCaffrey
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Daniel F. McCaffrey: Educational Testing Service

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 5, 544-575

Abstract: Many states and school districts in the United States use standardized test scores to compute annual measures of student achievement progress and then use school-level averages of these growth measures for various reporting and diagnostic purposes. These aggregate growth measures can vary consequentially from year to year for the same school, complicating their use and interpretation. We develop a method, based on the theory of empirical best linear prediction, to improve the accuracy and stability of aggregate growth measures by pooling information across grades, years, and tested subjects for individual schools. We demonstrate the performance of the method using both simulation and application to 6 years of annual growth measures from a large, urban school district. We provide code for implementing the method in the package schoolgrowth for the R environment.

Keywords: achievement growth; school accountability; linear mixed-effects models; variance components (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:5:p:544-575

DOI: 10.3102/10769986221101624

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