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.

摘要

教育研究中常用的许多分析模型通常旨在最大化解释方差并确定模型中的变量重要性。这些模型对于理解一般想法和趋势很有用，但对所述模型中的个体的洞察力有限。数据包络分析 (DEA) 是一种植根于组织管理的方法，它使这种洞察力成为可能。与上面提到的模型不同，DEA 不解释方差。相反，它解释了个人如何有效地利用他们的输入来产生输出，并确定哪些输入没有得到最佳利用。本文从教育以外的领域提供了 DEA 的历史和用法，并描述了其背后的数学和过程。然后，本文通过对儿童阅读能力的研究，将 DEA 的应用扩展到教育领域。n = 1987 )，DEA 使用简单的阅读框架视图进行演示，确定使用基于阅读的技能来实现阅读理解的个人效率水平，确定哪些技能未被充分利用，并对新的读者子集进行分类。使用这种方法确定了新的读者子集，这对更有针对性的干预措施产生了影响。