The article reviews a family of multilevel models that can be used to build general theories of the nonprofit sector that are still sensitive to variations in context. The comparative study of the nonprofit (or nongovernmental) sector presents formidable challenges to social scientists who are attempting to advance theory on the sector. Ostensibly, the goal is to model and test theories that are generalizable. Yet, as scholars study topics such as volunteerism, donations, governance, management, advocacy, accountability, and the like in different political, economic, and cultural contexts, they often find different patterns across cases. After reviewing the issues and introducing the idea that time (or more specifically events) can be thought of as context as well, we present an analytical approach for doing comparative research using the framework of hierarchical linear modeling.
Forecasting extremely rare events is a pressing problem, but efforts to model such outcomes are often limited by the presence of multiple causes within classes of events, insufficient observations of the outcome to assess fit, and biased estimates due to insufficient observations of the outcome. We introduce a novel approach for analyzing rare event data that addresses these challenges by turning attention to the conditions under which rare outcomes do not occur. We detail how configurational methods can be used to identify conditions or sets of conditions that would preclude the occurrence of a rare outcome. Results from Monte Carlo experiments show that our approach can be used to systematically preclude up to 78.6% of observations, and application to ground-truth data coupled with a bootstrap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses.
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