Author ORCID Identifier
Matthew Ingram: https://orcid.org/0000-0001-6904-2178
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.
Ingram, Matthew C. and Harbers, Imke, "Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data" (2017). Political Science Faculty Scholarship. 3.
LicenseStandard Author License
This is the Author's Accepted Manuscript. The version of record can be found here: Harbers, Imke, and Matthew C. Ingram. 2017. “Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data.” Political Analysis 25 (3): 289–307. doi:10.1017/pan.2017.4.