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dc.contributor.authorPrimo, David M.eng
dc.contributor.authorJacobsmeier, Matthew L.eng
dc.contributor.authorMilyo, Jeffreyeng
dc.description.abstractResearchers often seek to understand the effects of state policies or institutions on individual behavior or other outcomes in sub-state-level observational units (e.g., election results in state legislative districts). However, standard estimation methods applied to such models do not properly account for the clustering of observations within states and may lead researchers to overstate the statistical significance of state-level factors. We discuss the theory behind two approaches to dealing with clustering—clustered standard errors and multilevel modeling. We then demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005). While we view clustered standard errors as a more straightforward, feasible approach, especially when working with large datasets or many cross-level interactions, our purpose in this Practical Researcher piece is to draw attention to the issue of clustering in state and local politics research.eng
dc.identifier.citationDepartment of Economics, 2006eng
dc.publisherDepartment of Economicseng
dc.relation.ispartofEconomics publicationseng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Economicseng
dc.relation.ispartofseriesWorking papers (Department of Economics);WP 06-03eng
dc.subject.lcshMultilevel models (Statistics)eng
dc.titleEstimating the Impact of State Policies and Institutions with Mixed-Level Dataeng
dc.typeWorking Papereng

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