Integrating Large-Scale Online Surveys and Aggregate Data at the Constituency Level: The Estimation of Voter Transitions in the 2015 British General Elections

  • Paul W. Thurner Ludwig-Maximilians-University Munich (LMU)
  • Ingrid Mauerer Department of Political Science, LMU Munich
  • Maxim Bort Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich
  • André Klima Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich
  • Helmut Küchenhoff Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich
Keywords: voter transitions, ecological inference, British election study internet panel, hybrid models

Abstract

What have been the underlying voter shifts that led to the victory of the Con-servative Party in the 2015 British general election – against all predictions bypollsters? Analyses of voter transitions based on (online) surveys and recall ques-tions are plagued by sampling and response biases, whereas aggregate data analysesare suspect of the well-known ecological fallacy. We propose a systematic statisticalcombination of individual and aggregate data at the constituency level to identifyregional electoral shifts between the 2010 to 2015 British general elections, with aparticular focus on England. Large-scale individual data collected by the BritishElection Study Internet Panel (BESIP) allow us to locate more than 28,000 respon-dents in their constituencies. We estimate transitions based on a recently developedBayesian Hierarchical Hybrid Multinomial Dirichlet (HHMD) model. We discovera clear deviance from pure RxC ecological inference and from pure online panel-based estimations of transition matrices. Convergence diagnostics corroborate thesuperiority of the hybrid models.
Published
2020-09-25
How to Cite
Thurner, P. W., Mauerer, I., Bort, M., Klima, A., & Küchenhoff, H. (2020). Integrating Large-Scale Online Surveys and Aggregate Data at the Constituency Level: The Estimation of Voter Transitions in the 2015 British General Elections. Survey Research Methods, 14(5), 461-476. https://doi.org/10.18148/srm/2020.v14i5.7628
Section
Articles