Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates. (December 2019)

• Record Type: Journal Article
• Authors: Aparicio, Juan; Cordero, Jose M.; Ortiz, Lidia
• Is Part Of: Socio-economic planning sciences. Number 68(2019)
• Publication Date: 2019-12
• Subjects: Efficiency -- Data envelopment analysis -- Fuzzy data -- Education; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2019.03.004
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: Many studies devoted to efficiency performance evaluation in the education sector are based on measures of central tendency at school level as, for example, the average values of students belonging to the same school. Although this is a common and accepted way of summarizing data from the original observations (students), it is not less true that this approach neglects the existing dispersion of data, which may become a serious problem if variability across schools is high. Additionally, imprecision may arise when experts on each evaluated subject select the battery of questions, with different levels of difficulty, which will be the base for the final questionnaires completed by students. This paper uses data from US students and schools participating in PISA (Programme for International Student Assessment) 2015 to illustrate that schools' efficiency measures based on aggregate data and imprecision may reflect an inaccurate picture of their performance if they are compared to measures estimated accounting for broader information provided by all students of the same school. In order to operationalize our approach, we resort to Fuzzy Data Envelopment Analysis. This methodology allows us to deal with the notion of fuzziness in some variables such as the socio-economic status of students or test scores. Our results indicate that the estimated measures of performance obtained with the fuzzy DEA approach are highly correlated with those calculated with traditional DEA models. However, we find some relevant divergences in the identification of efficient units when we account for data dispersion and vagueness. Highlights: The influence of imprecision and variability in data on DEA estimates is shown. This issue is particularly important for measuring inefficiency in education. A way of considering imprecision and variability is through Fuzzy DEA. The results are compared with those obtained from applying stochastic DEA. Some divergences when one accounts for data dispersion and vagueness are detected.