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date: 18 January 2020

Abstract and Keywords

This article first presents a brief overview of the major types of multilevel statistical models. It reviews two major approaches to multilevel modeling available to researchers today: cross-sectional and longitudinal. It describes the case for their theoretical and conceptual value in the study of American bureaucracy and their potential technical advantages over more conventional approaches to statistical estimation of relationships in governmental systems. Next, the methodological critiques and limitations of multilevel modeling techniques are addressed. In particular, three of the major methodological critiques of multilevel models are explained: the causal inference problem, the argument that these models offer no net advantage, and cross-discipline transferability critiques. Finally, the article determines some of the most promising avenues for the future application of multilevel models. These include their use to promote more effective accountability systems by emphasizing performance analysis, to improve modeling of interorganizational relationships, and to investigate cross-level relationships.

Keywords: multilevel statistical models, American bureaucracy, multilevel modeling, causal inference problem, cross-discipline transferability

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