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

Autor:	J. R. Lockwood, Katherine E. Castellano, Daniel F. McCaffrey
Rok vydání:	2022
Předmět:	Social Sciences (miscellaneous) Education
Zdroj:	Journal of Educational and Behavioral Statistics. 47:544-575
ISSN:	1935-1054 1076-9986
DOI:	10.3102/10769986221101624
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3152d6f56db5a1ac2014f625882bf0a5 https://doi.org/10.3102/10769986221101624 Zobrazit plný text záznamu