ABSTRACT

Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating analyses to obtain results that meet the criteria thought to be required for publication, and the disproportionate reporting of false-positive results in the academic literature. This article proposes that the specification curve – a meta-analytical technique that visualizes the specifications and results from a large set of justifiable and plausible statistical models – be used as a pedagogical tool to teach (spatial) data analysis and explore the geographic uncertainties that arise when specifying and interpreting spatial regression models. An example specification curve that focuses on two common specification decisions in a spatial regression model, specifically selecting predictor variables and constructing the spatial weight matrix, is illustrated. Strategies for using the specification curve in educational contexts to develop analytical plans, reflect on the generalizability of research findings, and highlight issues of replicability and publication bias are proposed.

摘要

侧重于空间数据分析的教育材料通常以数学方法描述和软件工具的逐步说明为特色，但很少讨论指定统计模型所涉及的一组决策。不考虑模型规范可能会导致规范搜索，或重复分析以获得符合被认为发布所需标准的结果的过程，以及学术文献中假阳性结果的不成比例报告。本文建议将规格曲线——一种元分析技术，从大量合理且合理的统计模型中可视化规格和结果——用作教授（空间）数据分析和探索出现的地理不确定性的教学工具在指定和解释空间回归模型时。说明了一个示例规范曲线，该曲线侧重于空间回归模型中的两个常见规范决策，特别是选择预测变量和构建空间权重矩阵。提出了在教育环境中使用规范曲线来制定分析计划、反映研究结果的普遍性以及突出可复制性和发表偏倚问题的策略。说明了一个示例规范曲线，该曲线侧重于空间回归模型中的两个常见规范决策，特别是选择预测变量和构建空间权重矩阵。提出了在教育环境中使用规范曲线来制定分析计划、反映研究结果的普遍性以及突出可复制性和发表偏倚问题的策略。说明了一个示例规范曲线，该曲线侧重于空间回归模型中的两个常见规范决策，特别是选择预测变量和构建空间权重矩阵。提出了在教育环境中使用规范曲线来制定分析计划、反映研究结果的普遍性以及突出可复制性和发表偏倚问题的策略。