Hostname: page-component-74656f9884-npj9w Total loading time: 0 Render date: 2026-11-10T15:54:38.068Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls

Published online by Cambridge University Press:  04 January 2017

David K. Park ,
Andrew Gelman  and
Joseph Bafumi
David K. Park
Affiliation:
Department of Political Science and Applied Statistics, Washington University, St. Louis, MO 63130. e-mail: dpark@artsci.wustl.edu
Andrew Gelman
Affiliation:
Departments of Statistics and Political Science, Columbia University, New York, NY 10027. e-mail: gelman@stat.columbia.edu
Joseph Bafumi
Affiliation:
Department of Political Science, Columbia University, New York, NY 10027
Get access Rights & Permissions [Opens in a new window]

Abstract

We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997, Survey Methodology 23:127–135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the usual demographic variables. We evaluate the model by comparing it to state-level election outcomes. The multilevel model outperforms more commonly used models in political science. We envision the most important usage of this method to be not forecasting elections but estimating public opinion on a variety of issues at the state level.

Information

Type
Research Article
Information
Political Analysis , Volume 12 , Issue 4: Special Issue on Bayesian Methods , Autumn 2004 , pp. 375 - 385
Copyright
Copyright © Society for Political Methodology 2004 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Berry, W. D., Ringquist, E. J., Fording, R. C., and Hanson, R. L. 1998. “Measuring Citizen and Government Ideology in the American States, 1960–93.” American Journal of Political Science 42: 327–348.Google Scholar
Brace, P., Sims-Butler, K., Arcenaux, K., and Johnson, M. 2002. “Public Opinion in the American States: New Perspectives Using National Survey Data.” American Journal of Political Science 46: 173–189.Google Scholar
Bryk, A. S., and Raudenbush, S. W. 2001. Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Thousand Oaks, CA: Sage.Google Scholar
Burdick, E. 1964. The 480. New York: McGraw-Hill.Google Scholar
Erikson, R. S., Wright, G. C., and McIver, J. P. 1993. Statehouse Democracy: Public Opinion and Policy in the American States. Cambridge: Cambridge University Press.Google Scholar
Gelman, A. 2003. Bugs. R: Functions for Calling Bugs from R. (Available from http://www.stat.columbia.edu/∼gelman/bugsR.)Google Scholar
Gelman, A. 2004. “Prior Distributions for Variance Parameters in Hierarchical Models.” Unpublished manuscript.Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2003. Bayesian Data Analysis, 2nd ed. London: Chapman and Hall.Google Scholar
Gelman, A., and King, G. 1993. “Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?” British Journal of Political Science 23: 409–451.Google Scholar
Gelman, A., and Little, T. C. 1997. “Postratification into Many Categories Using Hierarchical Logistic Regression.” Survey Methodology 23: 127–135.Google Scholar
Jackman, S., and Rivers, D. 2001. “State Level Election Forecasting during Election 2000 via Dynamic Bayesian Hierarchical Modeling.” Presented at the Annual Meeting of the American Political Science Association, San Francisco, CA.Google Scholar
Kreft, I. G. G., and de Leeuw, J. 1998. Introducing Multilevel Modeling. Thousand Oaks, CA: Sage.Google Scholar
Park, D. 2004. “Multilevel Models of Representation in the U.S. States.” Ph.D. dissertation. Department of Political Science, Columbia University.Google Scholar
Pool, I. d. S., Abelson, R. P., and Popkin, S. L. 1965. Candidates, Issues, and Strategies. Cambridge, MA: MIT Press.Google Scholar
R Development Core Team. 2003. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. (Available from http://www.R-project.org.)Google Scholar
Snijders, T. A. B., and Bosker, R. 1999. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks, CA: Sage.Google Scholar
Spiegelhalter, D., Thomas, A., and Best, N. 1999. WinBugs Version 1.4. Cambridge, UK: MRC Biostatistics Unit.Google Scholar
Voss, D., Gelman, A., and King, G. 1995. “Preelection Survey Methodology: Details from Eight Polling Organizations, 1988 and 1992.” Public Opinion Quarterly 59: 98–132.CrossRefGoogle Scholar
Weber, R. E., Hopkins, A. H., Mezey, M. L., and Munger, F. 1972–1973. “Computer Simulation of State Electorates.” Public Opinion Quarterly 36: 49–65.CrossRefGoogle Scholar