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

Abstract

Mixed-methods designs have become increasingly popular. Lieberman (2005), for instance, advocates “nested analysis”, where cases for small-N analysis (SNA) are selected based on a large-N analysis (LNA). 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. Within traditional nested analysis, if process tracing during the SNA discovers diffusion as a causal mechanism, the LNA is disconnected from the SNA. Extending Lieberman’s nested analysis to spatially dependent data, we outline a framework for “geo-nested analysis” – where case selection for SNA is based on diagnostics of a spatial-econometric analysis. We illustrate this strategy using data from a seminal study of homicides in the United States.