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Comparing Strategies for Estimating Constituency Opinion from National Survey Samples

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  • Hanretty, Chris
  • Lauderdale, Benjamin E.
  • Vivyan, Nick

Abstract

Political scientists interested in estimating how public opinion varies by constituency have developed several strategies for supplementing limited constituency survey data with additional sources of information. We present two evaluation studies in the previously unexamined context of British constituency-level opinion: an external validation study of party vote share in the 2010 general election and a cross-validation of opinion toward the European Union. We find that most of the gains over direct estimation come from the inclusion of constituency-level predictors, which are also the easiest source of additional information to incorporate. Individual-level predictors combined with post-stratification particularly improve estimates from unrepresentative samples, and geographic local smoothing can compensate for weak constituency-level predictors. We argue that these findings are likely to be representative of applications of these methods where the number of constituencies is large.

Suggested Citation

  • Hanretty, Chris & Lauderdale, Benjamin E. & Vivyan, Nick, 2018. "Comparing Strategies for Estimating Constituency Opinion from National Survey Samples," Political Science Research and Methods, Cambridge University Press, vol. 6(3), pages 571-591, July.
  • Handle: RePEc:cup:pscirm:v:6:y:2018:i:03:p:571-591_00
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    Cited by:

    1. Hanretty, Chris, 2021. "Forecasting multiparty by-elections using Dirichlet regression," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1666-1676.
    2. 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.
    3. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    4. Eleonora Alabrese & Thiemo René Fetzer & Thiemo Fetzer, 2018. "Who is NOT Voting for Brexit Anymore?," CESifo Working Paper Series 7389, CESifo.
    5. Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.
    6. Saville, Christopher W N & Mann, Robin, 2022. "Cross-level group density interactions on mental health for cultural, but not economic, components of social class," Social Science & Medicine, Elsevier, vol. 296(C).
    7. Marina Christofoletti & Tânia R. B. Benedetti & Felipe G. Mendes & Humberto M. Carvalho, 2021. "Using Multilevel Regression and Poststratification to Estimate Physical Activity Levels from Health Surveys," IJERPH, MDPI, vol. 18(14), pages 1-16, July.

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