Replication Data for: Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["case selection","GWR-SE","GWR-SL","mixed-methods","spatial"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 
case selection; GWR-SE; GWR-SL; mixed-methods; spatial