Ordinal Approaches to Decomposing Between-Group Test Score Disparities

Autor/inn/en	Quinn, David M.; Ho, Andrew D.
Titel	Ordinal Approaches to Decomposing Between-Group Test Score Disparities
Quelle	In: Journal of Educational and Behavioral Statistics, 46 (2021) 4, S.466-500 (35 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620967726
Schlagwörter	Scores; Tests; Achievement Gap; Equal Education; Test Bias; Computation; Simulation; Racial Differences + Suchen Sie Ihr Suchwort? Examination; Prüfung; Examen; Testkritik; Simulation program; Simulationsprogramm; Rassenunterschied
Abstract	The estimation of test score "gaps" and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01