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

  • Imke Harbers (a1) and Matthew C. Ingram (a2)
Abstract

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.

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* Email: i.harbers@uva.nl
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Authors’ note: Previous versions of this paper were presented at faculty seminars at the University of Amsterdam, at an ECPR Joint Sessions workshop on spatial and network dependence convened by Jos Elkink and Kristian Gleditsch, and at the 2015 meeting of the American Political Science Association. We would like to thank audiences and discussants in these settings, especially Kyle Beardsley, Andrew Bennett, Brian Burgoon, Gustavo Flores-Macias, Lee Seymour, and Steve Wuhs for their suggestions. We gratefully acknowledge helpful feedback from the journal’s anonymous reviewers and editor, as well as from Glenn Deane and Tse-Chuan Yang, who commented on early drafts. We thank the National Consortium on Violence Research (NCOVR) for making available the county-level homicide data and Luc Anselin for making data so accessible (http://spatial.uchicago.edu/sample-data). Replication materials (data and code) are available at Harvard Dataverse, doi:10.7910/DVN/HRLHA4 (Harbers and Ingram 2016). Both authors contributed equally, and all remaining errors are their own.

Contributing Editor: Jonathan Katz

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