Data envelopment analysis (DEA) in the educational sciences

• Published in: The Journal of experimental education Vol. 90; no. 4; pp. 1021 - 1040
• Main Authors: Shero, Jeffrey A., Al Otaiba, Stephanie, Schatschneider, Chris, Hart, Sara A.
• Format: Journal Article
• Language: English
• Published: Washington Routledge 2022; Taylor & Francis Inc
• Subjects: Achievement Tests; Children; Classification; Data Analysis; Data envelopment analysis; Data Use; Decoding (Reading); Education; Educational Research; Efficiency; Input Output Analysis; Intervention; non-parametric; Nonparametric Statistics; reading; Reading Ability; Reading Comprehension; Reading Skills
• ISSN: 0022-0973; 1940-0683
• DOI: 10.1080/00220973.2021.1906198

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.