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Introduction to the Special Issue

Published online by Cambridge University Press:  04 January 2017

Orit Kedar and
W. Phillips Shively
Orit Kedar
Affiliation:
Department of Political Science, University of Michigan, ISR, 426 Thompson Street, P.O. Box 1248, Ann Arbor, MI 48106. e-mail: oritk@umich.edu
W. Phillips Shively
Affiliation:
Department of Political Science, University of Minnesota, 1414 Social Sciences Building, 267 19th Avenue South, Minneapolis, MN 55455. e-mail: shively@polisci.umn.edu
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Extract

The use of multilevel models—models in which lower-level (“micro”) units are nested within higher-level (“macro”) units—has blossomed recently in political science. Possible relationships in such models include macro variables influencing macro variables; micro variables influencing micro variables; macro variables influencing micro variables, and vice versa; and often most interestingly, micro-micro relationships varying interactively with macro variables. Most work in political science has drawn on the useful introductions of Raudenbush and Bryk (2002), Western (1998), and Steenbergen and Jones (2002). We refer readers to good general introductions/reviews of multi-level modeling in the articles in this issue by Bowers and Drake and by Franzese.

Information

Type
Research Article
Information
Political Analysis , Volume 13 , Issue 4: Special Issue on Multilevel Modeling for Large Clusters , Autumn 2005, pp. 297 - 300
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Cohen, Jeffrey E. 2004. “Economic Perceptions and Executive Approval in Comparative Perspective.” Political Behavior 26: 27–43.Google Scholar
Raudenbush, S., and Bryk, A. S. 2002. Hierarchical Linear Models, 2nd ed. Newbury Park, CA: Sage.Google Scholar
Steenbergen, Marco R., and Jones, Bradford. 2002. “Modeling Multilevel Data Structures.” American Journal of Political Science 46: 218–237.Google Scholar
Western, Bruce. 1998. “Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modeling Approach.” American Journal of Political Science 42: 1233–1259.Google Scholar