Practical Differences Among Aggregate-Level Conditional Status Metrics: From Median Student Growth Percentiles to Value-Added Models

Autor/inn/en	Castellano, Katherine E.; Ho, Andrew D.
Titel	Practical Differences Among Aggregate-Level Conditional Status Metrics: From Median Student Growth Percentiles to Value-Added Models
Quelle	In: Journal of Educational and Behavioral Statistics, 40 (2015) 1, S.35-68 (34 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998614548485
Schlagwörter	Expectation; Scores; Academic Achievement; Achievement Gains; Measurement; Regression (Statistics); Classification; Robustness (Statistics); Longitudinal Studies; Correlation; Models; Differences + Suchen Sie Ihr Suchwort? Expectancy; Erwartung; Schulleistung; Achievement gain; Leistungssteigerung; Messverfahren; Regression; Regressionsanalyse; Classification system; Klassifikation; Klassifikationssystem; Widerstandsfähigkeit; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Korrelation; Analogiemodell; Unterscheiden
Abstract	Aggregate-level conditional status metrics (ACSMs) describe the status of a group by referencing current performance to expectations given past scores. This article provides a framework for these metrics, classifying them by aggregation function (mean or median), regression approach (linear mean and nonlinear quantile), and the scale that supports interpretations (percentile rank and score scale), among other factors. This study addresses the question "how different are these ACSMs?" in three ways. First, using simulated data, it evaluates how well each model recovers its respective parameters. Second, using both simulated and empirical data, it illustrates practical differences among ACSMs in terms of pairwise rank differences incurred by switching between metrics. Third, it ranks ACSMs in terms of their robustness under scale transformations. The results consistently show that choices between mean- and median-based metrics lead to more substantial differences than choices between fixed- and random-effects or linear mean and nonlinear quantile regression. The findings set expectations for cross-metric comparability in realistic data scenarios. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01