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Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information

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  • Selb, Peter
  • Munzert, Simon

Abstract

Measures of constituency preferences are of vital importance for the study of political representation and other research areas. Yet, such measures are often difficult to obtain. Previous survey-based estimates frequently lack precision and coverage due to small samples, rely on questionable assumptions or require detailed auxiliary information about the constituencies' population characteristics. We propose an alternative Bayesian hierarchical approach that exploits minimal geographic information readily available from digitalized constituency maps. If at hand, social background data are easily integrated. To validate the method, we use national polls and district-level results from the 2009 German Bundestag election, an empirical case for which detailed structural information is missing.

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  • Selb, Peter & Munzert, Simon, 2011. "Estimating Constituency Preferences from Sparse Survey Data Using Auxiliary Geographic Information," Political Analysis, Cambridge University Press, vol. 19(4), pages 455-470.
    • Handle: RePEc:cup:polals:v:19:y:2011:i:04:p:455-470_01
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    Cited by:

    1. Christopher Claassen & Richard Traunmüller, 2020. "Improving and Validating Survey Estimates of Religious Demography Using Bayesian Multilevel Models and Poststratification," Sociological Methods & Research, , vol. 49(3), pages 603-636, August.
    2. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    3. Lauderdale, Benjamin E. & Bailey, Delia & Blumenau, Jack & Rivers, Douglas, 2020. "Model-based pre-election polling for national and sub-national outcomes in the US and UK," International Journal of Forecasting, Elsevier, vol. 36(2), pages 399-413.
    4. Jonathan Gellar & Sarah Hughes & Constance Delannoy & Erin Lipman & Shirley Jeoffreys-Leach & Bobby Berkowitz & Grant J. Robertson, "undated". "Calibrated Multilevel Regression with Poststratifiction for the Analysis of SMS Survey Data," Mathematica Policy Research Reports c71d456bbf9f4026988e1a810, Mathematica Policy Research.

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