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

48 Pages Posted: 30 Sep 2015

See all articles by Imke Harbers

Imke Harbers

University of Amsterdam

Matthew C. Ingram

SUNY University at Albany

Date Written: September 28, 2015

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.

Keywords: spatial analysis, mixed methods, GWR

JEL Classification: C00, C49

Suggested Citation

Harbers, Imke and Ingram, Matthew C., Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data (September 28, 2015). Available at SSRN: https://ssrn.com/abstract=2666725 or http://dx.doi.org/10.2139/ssrn.2666725

Imke Harbers

University of Amsterdam ( email )

Matthew C. Ingram (Contact Author)

SUNY University at Albany ( email )

135 Western Ave
Milne Hall 314-A
Albany, NY 12222
United States

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