Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-27T20:33:15.364Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimating the Political Center from Aggregate Data: An Item Response Theory Alternative to the Stimson Dyad Ratios Algorithm

Published online by Cambridge University Press:  04 January 2017

Anthony J. McGann
Anthony J. McGann*
Affiliation:
School of Government and Public Policy, University of Strathclyde, McCance Building, 16 Richmond Street, Glasgow G1 1XQ, United Kingdom, and Department of Political Science, University of California, Irvine, 3151 Social Science Plaza, Irvine, CA 92697-5100 email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This article provides an algorithm to produce a time-series estimate of the political center (or median voter) from aggregate survey data, even when the same questions are not asked in most years. This is compared to the existing Stimson dyad ratios approach, which has been applied to various questions in political science. Unlike the dyad ratios approach, the model developed here is derived from an explicit model of individual behavior—the widely used item response theory model. I compare the results of both techniques using the data on public opinion from the United Kingdom from 1947 to 2005 from Bartle, Dellepiane-Avellaneda, and Stimson. Measures of overall model fit are provided, as well as techniques for testing model's assumptions and the fit of individual items. Full code is provided for estimation with free software WinBUGS and JAGS.

Type
Research Article
Information
Political Analysis , Volume 22 , Issue 1 , Winter 2014 , pp. 115 - 129
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

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.)

References

Bafumi, J., Gelman, A., Park, D. K., and Kaplan, N. 2005. Practical issues in implementing and understanding Bayesiain ideal point estimation. Political Analysis 13(2): 171–87.Google Scholar
Bafumi, J., and Herron, M. C. 2010. Leapfrog representation and extremism: A study of American voters and their members in Congress. American Political Science Review 104(3): 519–42.CrossRefGoogle Scholar
Bartle, John, Dellepiane-Avellaneda, Sebastian, and Stimson, James. 2011a. The moving centre: Preferences for government activity in Britain, 1950–2005. British Journal of Political Science 41(2): 259–85.Google Scholar
Bartle, John, Dellepiane-Avellaneda, Sebastian, and Stimson, James A. 2011b. The policy mood and the moving centre. In Britain at the polls 2010, eds. Allen, N. and Bartle, J. London: SAGE.Google Scholar
Baumgartner, Frank R., De Boef, Suzanna L., and Boydstun, Amber E. 2008. The decline of the death penalty and the discovery of innocence. Cambridge, UK: Cambridge University Press.Google Scholar
Chanley, Virginia A., Rudolph, Thomas J., and Rahn, Wendy M. 2000. The origins and consequences of public trust in government: A time series analysis. Public Opinion Quarterly 64: 239–56.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. The statistical analysis of roll call data. American Political Science Review 98(2): 355–70.Google Scholar
Cohen, Jeffrey E. 2000. The polls: Public favorability towards the First Lady. Presidential Studies Quarterly 30: 575–85.Google Scholar
Erikson, Robert S., MacKuen, Michael, and Stimson, James A. 2002. The macro polity, Cambridge studies in political psychology and public opinion. New York: Cambridge University Press.Google Scholar
Gelman, Andrew, Carlin, John B., Stern, Hal S., and Rubin, Donald B. 2004. Bayesian data analysis. 2nd ed., Texts in statistical science. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Jackman, Simon. 2000. Estimation and inference are missing data problems: Unifying social science statistics via Bayesian simulation. Political Analysis 8(4): 307–32.Google Scholar
Jackman, Simon. 2001. Multidimensional analysis of roll call data via Bayesian simulation: Identification, estimation, inference and model checking. Political Analysis 9(3): 227–41.Google Scholar
Jackman, Simon. 2005. Pooling the polls over an election campaign. Australian Journal of Political Science 40(4): 499–517.Google Scholar
Jessee, S. A. 2009. Spatial voting in the 2004 presidential election. American Political Science Review 103(1): 59–81.Google Scholar
Kellstadt, Paul. 2003. The mass media and the dynamics of American racial attitudes. Cambridge, UK: Cambridge University Press.Google Scholar
Kim, Hee Min, and Fording, Richard. 2001. Extending party estimates to voters and governments. In Mapping policy preferences: Estiamtes for parties, electors, and governemnts 1945–1998, eds. Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., and Tanenbaum, E. Oxford: Oxford University Press.Google Scholar
Levendusky, M. S., and Pope, J. C. 2010. Measuring aggregate-level ideological heterogeneity. Legislative Studies Quarterly 35(2): 259–82.Google Scholar
Marascuilo, Leonard, and Slaughter, Robert. 1981. Statistical procedures for identifying possible sources of item bias based on χ2 statistics. Journal of Educational Measurement 18(4): 229–48.Google Scholar
Martin, Andrew D., and Quinn, Kevin M. 2002. Dynamic ideal point estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999. Political Analysis 10(2): 134–53.Google Scholar
McDonald, Michael, and Budge, Ian. 2005. Elections, parties, democracy: Conferring the median mandate. Oxford: Oxford University Press.Google Scholar
McGann, Anthony. 2013. Replication data for: Estimating the Political Center from Aggregate Data: An Item Response Theory Alternative to the Stimson Dyad Ratios Algorithm. http://dx.doi.org/10.7910/DVN/22861 IQSS Dataverse Network [Distributor] V1 [Version].Google Scholar
Nunnally, Jum C., and Bernstein, Ira H. 1994. Psychometric theory. 3rd ed. New York: McGraw-Hill.Google Scholar
Peress, Michael. 2009. Small chamber ideal point estimation. Political Analysis 17(3): 276–90.Google Scholar
Poole, Keith, and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. Oxford: Oxford University Press.Google Scholar
Powell, G. Bingham Jr. 2000. Elections as instruments of democracy: Majoritarian and proportional visions. New Haven, CT: Yale University Press.Google Scholar
Stimson, James A. 1991. Public opinion in America: Moods, cycles, and swings, transforming American politics. Boulder, CO: Westview Press.Google Scholar
Stimson, James A. 1999. Public opinion in America: Moods, cycles, and swings. 2nd ed. Transforming American politics. Boulder, CO: Westview Press.Google Scholar
Stimson, James A., Mackuen, Michael B., and Erikson, Robert S. 1995. Dynamic representation. American Political Science Review 89(3): 543–65.Google Scholar
Stimson, James, Thiébaut, Cyrille, and Tiberj, Vincent. 2009. The structure of policy attitudes in France. Paper presented at the Annual Meetings of the Midwest Political Science Association. Chicago.Google Scholar
Swaminathan, Hariharan, and Jane Rogers, H. 1990. Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement 27(4): 361–70.Google Scholar
Voeten, Erik, and Brewer, Paul R. 2006. Public opinion, the war in Iraq, and presidential accountability. Journal of Conflict Resolution 50: 809–30.Google Scholar
Supplementary material: File

McGann supplementary material

Appendix

Download McGann supplementary material(File)
File 687.6 KB