Conference on Hierarchical Modeling

Saturday, April 29, 2006

Sponsored by the Department of Political Science, University of North Texas

Conference Participants

• Raymond M. Duch, University of Houston: "Context and the Economic Vote"
• Wonjae Hwang, University of Tennessee: "Economic Integration, Preference Convergence, and Political Decisions"
• Eduardo Leoni, Columbia University: "Analyzing Cross-Country Survey Data: Results from Monte Carlo Experiments"
• Tetsuya Matsubayashi, Texas A&M University: "Registrants, Voters, and Turnout Variability Across Neighborhoods"
• Randy Stevenson, Rice University: Discussant

Hierarchical modeling, or multilevel modeling, is a cutting-edge statistical technique that has been attracting much attention recently. The method is used to analyze data in which observations are clustered into higher-level units (e.g., people and countries). Traditionally, such data were examined by employing dummy variables of higher-level units or by running a separate analysis for each group. The development of hierarchical modeling enables researchers to examine hierarchical data in a single comprehensive model and evaluate the dynamics of the variables of different levels. This conference features research that applies hierarchical modeling techniques to questions in American politics, comparative politics, and international relations.