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

Autor/inn/en	Lockwood, J. R.; Castellano, Katherine E.; McCaffrey, Daniel F.
Titel	Improving Accuracy and Stability of Aggregate Student Growth Measures Using Empirical Best Linear Prediction
Quelle	In: Journal of Educational and Behavioral Statistics, 47 (2022) 5, S.544-575 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (McCaffrey, Daniel F.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221101624
Schlagwörter	Accuracy; Prediction; Programming Languages; Standardized Tests; Scores; Academic Achievement; Measurement Techniques; Urban Schools; School Districts; Longitudinal Studies; Achievement Gains; Elementary Secondary Education; English; Language Arts; Mathematics Achievement; Accountability; Monte Carlo Methods; Kindergarten + Suchen Sie Ihr Suchwort? Vorhersage; Standadised tests; Standardisierter Test; Schulleistung; Messtechnik; Urban area; Urban areas; School; Schools; Stadtregion; Stadt; Schule; School district; Schulbezirk; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Achievement gain; Leistungssteigerung; English language; Englisch; Sprachkultur; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Verantwortung; Monte-Carlo-Methode
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01