Distributional Analysis in Educational Evaluation: A Case Study from the New York City Voucher Program

Autor/inn/en	Bitler, Marianne; Domina, Thurston; Penner, Emily; Hoynes, Hilary
Titel	Distributional Analysis in Educational Evaluation: A Case Study from the New York City Voucher Program
Quelle	In: Journal of Research on Educational Effectiveness, 8 (2015) 3, S.419-450 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2014.921259
Schlagwörter	Educational Assessment; School Choice; Educational Vouchers; Case Studies; Statistical Distributions; Statistical Significance; Scholarships; Academic Achievement; Hypothesis Testing; Intervention; Urban Schools; Scores; Statistical Analysis; Educational Experiments; Program Effectiveness; New York (New York); Early Childhood Longitudinal Survey; Iowa Tests of Basic Skills + Suchen Sie Ihr Suchwort? Education; assessment; Bewertungssystem; Choice of school; Schulwahl; Educational voucher; Bildungsgutschein; Case study; Fallstudie; Case Study; Wahrscheinlichkeitsverteilung; Scholarship; Stipendium; Schulleistung; Hypothesenprüfung; Hypothesentest; Urban area; Urban areas; School; Schools; Stadtregion; Stadt; Schule; Statistische Analyse; Schulversuch
Abstract	We use quantile treatment effects estimation to examine the consequences of the random-assignment New York City School Choice Scholarship Program across the distribution of student achievement. Our analyses suggest that the program had negligible and statistically insignificant effects across the skill distribution. In addition to contributing to the literature on school choice, the article illustrates several ways in which distributional effects estimation can enrich educational research: First, we demonstrate that moving beyond a focus on mean effects estimation makes it possible to generate and test new hypotheses about the heterogeneity of educational treatment effects that speak to the justification for many interventions. Second, we demonstrate that distributional effects can uncover issues even with well-studied data sets by forcing analysts to view their data in new ways. Finally, such estimates highlight where in the overall national achievement distribution test scores of children exposed to particular interventions lie; this is important for exploring the external validity of the intervention's effects. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11