Data Envelopment Analysis (DEA) in the Educational Sciences

Autor/inn/en	Shero, Jeffrey A.; Al Otaiba, Stephanie; Schatschneider, Chris; Hart, Sara A.
Titel	Data Envelopment Analysis (DEA) in the Educational Sciences
Quelle	In: Journal of Experimental Education, 90 (2022) 4, S.1021-1040 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shero, Jeffrey A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2021.1906198
Schlagwörter	Data Analysis; Educational Research; Nonparametric Statistics; Efficiency; Models; Input Output Analysis; Data Use; Children; Reading Ability; Reading Skills; Reading Comprehension; Classification; Intervention; Achievement Tests; Decoding (Reading); Woodcock Johnson Tests of Achievement + Suchen Sie Ihr Suchwort? Auswertung; Bildungsforschung; Pädagogische Forschung; Effectiveness; Effektivität; Wirkungsgrad; Analogiemodell; Child; Kind; Kinder; Reading competence; Lesekompetenz; Reading skill; Lesefertigkeit; Leseverstehen; Classification system; Klassifikation; Klassifikationssystem; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Dekodierung
Abstract	Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in organizational management that makes such insights possible. Unlike models alluded to above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides a history and usages of DEA from fields outside of education, and describes the math and processes behind it. This paper then extends DEA's usage into the educational field using a study on child reading ability. Using students from the Project KIDS dataset (n = 1987), DEA is demonstrated using a simple view of reading framework, identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying new subsets of readers. New subsets of readers were identified using this method, with implications for more targeted interventions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01