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Modeling Multilevel Data Structures Jones, Bradford S. Steenbergen, Marco R. Uploaded 07-17-1997 Keywords multilevel models random coefficients contextual analysis comparative Abstract Although integrating multiple levels of data into an analysis can often yield better inferences about the phenomenon under study, traditional methodologies used to combine multiple levels of data are problematic. In this paper, we discuss several methodologies under the rubric of multilevel analysis. Multilevel methods, we argue, provide researchers, particularly researchers using comparative data, substantial leverage in overcoming the typical problems associated with either ignoring multiple levels of data, or problems associated with combining lower-level and higher-level data (including overcoming implicit assumptions of fixed and constant effects). The paper discusses several variants of the multilevel model and provides an application of individual-level support for European integration using comparative political data from Western Europe.

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Fitting Multilevel Models When Predictors and Group Effects Correlate Bafumi, Joseph Uploaded 04-27-2006 Keywords Multilevel models random effects fixed effects unit effects group effects Gauss-Markov Abstract Random effects models (that is, regressions with varying intercepts that are modeled with error) are avoided by some social scientists because of potential issues with bias and uncertainty estimates. Particularly, when one or more predictors correlate with the group or unit effects, a key Gauss-Markov assumption is violated and estimates are compromised. However, this problem can easily be solved by including the average of each individual-level predictors in the group-level regression. We explain the solution, demonstrate its effectiveness using simulations, show how it can be applied in some commonly-used statistical software, and discuss its potential for substantive modeling.

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Public Opinion and Senate Confirmation of Supreme Court Nominees Kastellec, Jonathan Lax, Jeffrey Phillips, Justin Uploaded 08-22-2008 Keywords Supreme Court nominations public opinion multilevel models poststratification Abstract We study the relationship between state-level public opinion and the roll call votes of senators on Supreme Court nominees. Applying recent advances in multilevel modeling, we use national polls on nine recent Supreme Court nominees to produce state-of-the-art estimates of public support for the confirmation of each nominee in all 50 states. We show that greater public support strongly increases the probability that a senator will vote to approve a nominee, even after controlling for standard predictors of roll call voting. We also find that the impact of opinion varies with context: it has a greater effect on opposition party senators, on ideologically opposed senators, and for generally weak nominees. These results establish a systematic and powerful link between constituency opinion and voting on Supreme Court nominees.

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Cosponsorship in the U.S. Senate: A Multilevel Approach to Detecting Subtle Social Predictors of Legilslative Support Gross, Justin Uploaded 09-14-2008 Keywords Congress cosponsorship social network analysis multilevel models mixed effects GLMM Abstract Why do members of Congress choose to cosponsor legislation proposed by their colleagues and what can we learn from their patterns of cosponsorship? To answer these questions properly requires models that respect the relational nature of the relevant data and the resulting interdependence among observations. We show how the inclusion of carefully selected random effects can capture network-type dependence, allowing us to more confidently investigate senators' propensity to support colleagues' proposals. To illustrate, we examine whether certain social factors such as demographic similarities, opportunities for interaction, and institutional roles are associated with varying odds of cosponsorship during the 2003-04 (108th) Senate.