Integrating large-scale online surveys and aggregate data at the constituency level: The estimation of voter transitions in the 2015 british general elections

Thurner, Paul W.; Mauerer, Ingrid ; Bort, Maxim; Klima, Andre und Küchenhoff, Helmut (2020): Integrating large-scale online surveys and aggregate data at the constituency level: The estimation of voter transitions in the 2015 british general elections. In: Survey Research Methods, Bd. 14, Nr. 5: S. 461-476

Abstract

What have been the underlying voter shifts that led to the victory of the Conservative Party in the 2015 British general election – against all predictions by pollsters? Analyses of voter transitions based on (online) surveys and recall questions are plagued by sampling and response biases, whereas aggregate data analyses are suspect of the well-known ecological fallacy. We propose a systematic statistical combination of individual-level survey and administrative data at the constituency level to identify regional electoral shifts between the 2010 to 2015 British general elections. The large-scale individual-level data collected by the British Election Study Internet Panel (BESIP) allow us to locate more than 28,000 respondents in their constituencies. We estimate voter transitions based on a recently developed Bayesian Hierarchical Hybrid Multinomial Dirichlet (HHMD) model. We discover substantial deviances from pure survey-based estimations of transition matrices.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Publisher's Version
• Keywords: voter transitions; ecological inference; British election study internet panel; hybrid models
• Fakultät: Mathematik, Informatik und Statistik ; Mathematik, Informatik und Statistik > Statistik ; Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Statistisches Beratungslabor
• Themengebiete: 300 Sozialwissenschaften > 300 Sozialwissenschaft, Soziologie ; 300 Sozialwissenschaften > 310 Statistiken ; 300 Sozialwissenschaften > 320 Politik
• JEL Classification: B4, C1, C4, C6
• ISSN: 18643361
• Sprache: Englisch