This repository contains supplementary and replication materials for the paper "Electoral predictors of polling errors" by Sina Chen, John Körtner, Jens Wiederspohn, and Peter Selb.
Case studies of polling failures focus on within-election differences in poll accuracy. The crucial question of why polls fail in one election but not in others often remains a matter of speculation. To develop a contextual understanding, we review and unify the- ories of election features suspected of encouraging polling errors, including mobilization, candidacies, polarization, and electoral conduct. We extend a Bayesian hierarchical modeling approach that separates poll bias and variance at the election level and links error components to electoral predictors. Investigating 6,375 pre-election polls across 318 U.S. Senate elections, 1990-2022, we find an overall trend toward smaller but more uniform errors. Poll variance exhibits a weak negative association with mobilization and polarization. Until 2004, frontrunners and incumbents were overestimated, but there is little evidence that polls are biased for female or minority candidates. Finally, Republicans in states with lower levels of state democracy are slightly underestimated in recent years.
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The code folder contains all code required for replication.
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The preparation contains all code for prepare polling data and merge relevant covariates.
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The fit_stan contains all code for fitting the single models.
- In the subfolder stan_ml the stan models can be found.
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The results_vis folder contains all code necessary for visualizing the results.
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The data folder contains polling data for 2022 and all data on covariates. Polling data from 1990-2020 may be obtained from 538 on request.
The analysis was conducted with R version 4.3.1 and Stan version 2.26.1.
Sina Chen
Graduate School of the Social and Behavioural Sciences
University of Konstanz
Email: [email protected]