In:
Journal of Educational and Behavioral Statistics, American Educational Research Association (AERA), Vol. 47, No. 5 ( 2022-10), p. 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.
Type of Medium:
Online Resource
ISSN:
1076-9986
,
1935-1054
DOI:
10.3102/10769986221101624
Language:
English
Publisher:
American Educational Research Association (AERA)
Publication Date:
2022
detail.hit.zdb_id:
1225314-5
detail.hit.zdb_id:
2174169-4
SSG:
5,3
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