Replication Data for: Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data

Mixed-methods designs, especially those where cases selected for small-N analysis (SNA) are nested within a large-N analysis (LNA), have become increasingly popular. Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial dependence, and dependence becomes a threat to inference, rather than an issue for empirical or theoretical investigation. This is unfortunate, since research in political science has recently drawn attention to diffusion and interconnectedness more broadly. In this paper we develop a framework for mixed-methods research with spatially dependent data—a framework we label “geo-nested analysis”—where insights gleaned at each step of the research process set the agenda for the next phase and where case selection for SNA is based on diagnostics of a spatial-econometric analysis. We illustrate our framework using data from a seminal study of homicides in the United States.

混合方法研究设计，尤其是将小样本分析（small-N analysis, SNA）的案例嵌套于大样本分析（large-N analysis, LNA）中的设计，正日益受到学界青睐。然而，此类方法中的大样本分析假定分析单元独立分布，导致这类设计无法考量空间相关性（spatial dependence），此时空间相关性将对统计推断构成威胁，而非仅为经验或理论研究可探讨的议题。这一局限颇为遗憾，因为近年来政治学领域的研究愈发关注扩散效应与整体关联性。本文提出了一种适用于带空间相关性数据的混合方法研究框架——我们将其命名为"地理嵌套分析（geo-nested analysis）"——该框架中，研究各阶段所得洞见可为下一阶段设定研究议程，且小样本分析的案例选择基于空间计量分析（spatial-econometric analysis）的诊断结果。我们以美国一项开创性凶杀案研究的数据集为例，对该框架进行了演示说明。