Researchers and practitioners in education and school psychology regularly use ranked data to drive student- and systems-level decision-making. These types of data can be derived from assessments of individual preferences among researchers and practitioners, assessments of preferences among stakeholders including parents and children, and rankings of students on academic and social-emotional competency. However, the analysis of ranked data in education and psychology has typically been limited to simple approaches such as the examination of mean ranks assigned to items. This paper unifies a collection of classical methodologies, as well as proposes new techniques, for analyzing ranked data used across disciplines and applies the methods to data generated in school psychological research. The scope of the paper is to serve as a roadmap for researchers in education and school psychology who seek to more fully leverage information contained in ranked data. These methodologies include descriptive analyses, visualizations, tests of uniformity, cluster analyses, and predictive models. We demonstrate these techniques on the survey data of Fefer, DeMagistris, and Shuttleton (2016) and illustrate how using a broader set of tools can yield improved insights by researchers and practitioners.

教育和学校心理学领域的研究人员和从业人员经常使用排名数据来推动学生和系统级决策。这些类型的数据可以来自研究人员和从业者对个人偏好的评估、对包括父母和孩子在内的利益相关者的偏好评估以及学生在学术和社会情感能力方面的排名。然而，对教育和心理学中排名数据的分析通常仅限于简单的方法，例如检查分配给项目的平均排名。本文统一了一系列经典方法，并提出了新技术，用于分析跨学科使用的排名数据，并将这些方法应用于学校心理学研究中生成的数据。该论文的范围是为寻求更充分利用排名数据中包含的信息的教育和学校心理学研究人员提供路线图。这些方法包括描述性分析、可视化、一致性测试、聚类分析和预测模型。我们在 Fefer、DeMagistris 和 Shuttleton（2016 年）的调查数据上展示了这些技术，并说明了如何使用更广泛的工具集可以让研究人员和从业者获得更好的见解。