Publikation: How to Poll Runoff Elections
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2020
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Public Opinion Quarterly. Oxford University Press. 2020, 84(3), pp. 776-787. ISSN 0033-362X. eISSN 1537-5331. Available under: doi: 10.1093/poq/nfaa039
Zusammenfassung
We present a polling strategy to predict and analyze runoff elections using the 2017 French presidential race as an empirical case. This strategy employs rejective probability sampling to identify a small sample of polling stations that is balanced with respect to past election results. We then survey the voters’ candidate evaluations in first-round exit polls. We poststratify the voter sample to first-round election returns to account for nonresponse and coverage issues, and impute missing candidate evaluations to emulate campaign learning. Next, the votes for eliminated competitors are redistributed according to their supporters’ lower-order preferences. Finally, the predictions are validated against official results and other polls. We end with a discussion of the advantages and limitations of this approach.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
320 Politik
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SELB, Peter, Sascha GÖBEL, Romain LACHAT, 2020. How to Poll Runoff Elections. In: Public Opinion Quarterly. Oxford University Press. 2020, 84(3), pp. 776-787. ISSN 0033-362X. eISSN 1537-5331. Available under: doi: 10.1093/poq/nfaa039BibTex
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year={2020},
doi={10.1093/poq/nfaa039},
title={How to Poll Runoff Elections},
number={3},
volume={84},
issn={0033-362X},
journal={Public Opinion Quarterly},
pages={776--787},
author={Selb, Peter and Göbel, Sascha and Lachat, Romain},
note={Correction: https://doi.org/10.1093/poq/nfab019}
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<dcterms:abstract xml:lang="eng">We present a polling strategy to predict and analyze runoff elections using the 2017 French presidential race as an empirical case. This strategy employs rejective probability sampling to identify a small sample of polling stations that is balanced with respect to past election results. We then survey the voters’ candidate evaluations in first-round exit polls. We poststratify the voter sample to first-round election returns to account for nonresponse and coverage issues, and impute missing candidate evaluations to emulate campaign learning. Next, the votes for eliminated competitors are redistributed according to their supporters’ lower-order preferences. Finally, the predictions are validated against official results and other polls. We end with a discussion of the advantages and limitations of this approach.</dcterms:abstract>
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Prüfungsdatum der Dissertation
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Kommentar zur Publikation
Correction: https://doi.org/10.1093/poq/nfab019
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