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

Author

Listed:
  • J. R. Lockwood
  • Katherine E. Castellano
  • Daniel F. McCaffrey

    (Educational Testing Service)

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.

Suggested Citation

  • J. R. Lockwood & Katherine E. Castellano & Daniel F. McCaffrey, 2022. "Improving Accuracy and Stability of Aggregate Student Growth Measures Using Empirical Best Linear Prediction," Journal of Educational and Behavioral Statistics, , vol. 47(5), pages 544-575, October.
    • Handle: RePEc:sae:jedbes:v:47:y:2022:i:5:p:544-575
    DOI: 10.3102/10769986221101624
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