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Declining response rates and increasing costs have led to greater use of non-probability samples in election polling. But non-probability samples may suffer from selection bias due to differential access, degrees of interest and other factors. Here we estimate voting preference for 19 elections in the US 2014 midterm elections by using large non-probability surveys obtained from SurveyMonkey users, calibrated to estimated control totals using model-assisted calibration combined with adaptive LASSO regression, or the estimated controlled LASSO, ECLASSO. Comparing the bias and root-mean-square error of ECLASSO with traditional calibration methods shows that ECLASSO can be a powerful method for adjusting non-probability surveys even when only a small sample is available from a probability survey. The methodology proposed has potentially broad application across social science and health research, as response rates for probability samples decline and access to non-probability samples increases.

Voters’ beliefs about the strength of political parties are a central part of many foundational political science theories. In this article, we present a dynamic Bayesian learning model that allows us to study how voters form these beliefs by learning from pre-election polls over the course of an election campaign. In the model, belief adaptation to new polls can vary due to the perceived precision of the poll or the reliance on prior beliefs. We evaluate the implications of our model using two experiments. We find that respondents update their beliefs assuming that the polls are relatively imprecise but still weigh them more strongly than their priors. Studying implications for motivational learning by partisans, we find that varying adaptation works through varying reliance on priors and not necessarily by discrediting a poll’s precision. The findings inform our understanding of the consequences of learning from polls during political campaigns and motivational learning in general.

We introduce an electoral pollster in the canonical pivotal voting model and show that the misreporting of pre-election poll results can happen even in the absence of partisan motives, as long as reputational concerns are present. By underreporting the expected number of supporters of the most preferred candidate in society, the pollster can induce an election result more likely to be in line with its report. By doing so, not only victory chances of the most preferred candidate rise above 50%, thus breaking the unrealistic neutrality result of the pivotal voting model, but also total election costs are reduced, thus yielding welfare gains and partially offsetting the expected negative effect of polls on welfare (see Goeree and Großer in Econ Theory 31:51–68, 2007 ; Taylor and Yildirim in Games Econ Behav 68:353–375, 2010 ). Our model also allows for the simultaneous accommodation of the underdog effect (a feature of pivotal voting models) and the apparently inconsistent bandwagon effect, in the sense that the latter can actually be understood as an illusion due to the possibility of misreporting being overlooked. All of these results hold even as the electorate size grows without bound.

Ridge calibration is a penalized method used in survey sampling to reduce the variability of the final set of weights by relaxing the linear restrictions. We proposed a method for selecting the penalty parameter that minimizes the estimated mean squared error of the mean estimator when estimated auxiliary information is used. We showed that the proposed estimator is asymptotically equivalent to the generalized regression estimator. A simple simulation study shows that our estimator has the smaller MSE compared to the traditional calibration ones. We applied our method to predict election result using National Barometer Survey and Korea Social Integration Survey.

Accounting for undecided and uncertain voters is a challenging issue for predicting election results from public opinion polls. Undecided voters typify the uncertainty of swing voters in polls but are often ignored or allocated to each candidate in a simple deterministic manner. Historically this may have been adequate because the undecided voters were sufficiently small to assume that they do not affect the relative proportions of the decided voters. However, in the presence of high numbers of undecided voters, these static rules may in fact bias election predictions from election poll researchers and metapoll analysts. We examine the effect of undecided voters in the 2016 US presidential election compared with the previous three presidential elections. We show that there were a relatively high number of undecided voters over the campaign and on election day, and that the allocation of undecided voters in this election was not consistent with two-party proportional (or even) allocations. We find evidence that static allocation regimes are inadequate for election prediction models and that probabilistic allocations may be superior. We also estimate the bias attributable to polling agencies, which is often referred to as ‘house effects’.

As public confidence in polling has been waning in the wake of recent elections (Narea, N. 2016. After 2016, ? New Republic), many researchers have been seeking to diagnose the shortcomings in these data (Gelman, A., and J. Azari. 2017. “19 Things We Learned from the 2016 Election.” 4 (1): 1–10; Kennedy, C., M. Blumenthal, S. Clement, J. D. Clinton, C. Durand, C. Franklin, K. McGeeney, L. Miringoff, K. Olson, D. Rivers, L. Saad, G. E. Witt, and C. Wlezien. 2018. “An Evaluation of the 2016 Election Polls in the United States.” 82 (1): 1–33; Mercer, A., C. Deane, and K. McGeeney. 2016. . Also available at ). One conjecture stems from observed differences between polling results based on the methodological choice between live and non-live modes of survey administration (Enten, H. 2015. . FiveThirtyEight. Also available at ). While it has become commonplace to discuss “mode effect” on surveys, it reemerged in the political zeitgeist as the “Shy Trump” supporter hypothesis leading up to the 2016 U.S. Presidential Election (Edsall, T. B. 2016. . New York Times). Motivated by the conflicting evidence for (Enns, P. K., J. Lagodny, and J. P. Schuldt. 2017. “Understanding the 2016 US Presidential Polls: The Importance of Hidden Trump Supporters.” 8 (1): 41–63) and against (Coppock, A. 2017. “Did Shy Trump Supporters Bias the 2016 Polls? Evidence from a Nationally-Representative List Experiment.” 8 (1): 29–40) this hypothesis, we built a complex statistical model that pools together results across multiple pollsters and throughout the election cycle while accounting for the nuances of these data. Specifically, we explored election data for the presence of mode effect using time series with a general additive mixed model (GAMM). We estimated mode effect at state and national levels to perform statistical mode adjustments, which we then compared to observed election results. In this paper, we utilized polling results from the United States Presidential Elections in 2016 (4208 polls) and 2020 (4133 polls). Using these data, we identified spatial trends and areas where mode effect was statistically significant at a 0.05 level. In summary, we make three contributions to the literature on mode effect adjustment in the poll aggregation setting. First, we present a straightforward and flexible statistical approach to estimating mode effect using time series data. In doing so, we help to bridge the gap between theory-focused statistical work and the social sciences. Second, we apply this method to two recent presidential elections, providing insight into the significance of mode effect. Third, we provide evidence for spatial mode effect trends suggesting regional voting behaviors that future scholars can explore.

The main purpose of this paper is to use the Spanish case, through an econometric analysis of 226 electoral polls, to explain why polls are making more mistakes in times of great socioeconomic slumps, political instability and the emergence of new political parties. In this context, it is the very instrument with which society tries to reduce the reigning uncertainty that, paradoxically, can ultimately drive uncertainty up. Our results show that the prediction error for the new emerging parties is significantly higher than for the traditional parties and this error is not sensitive to solutions for increasing the reliability of surveys, such as increasing sample size, transparency constantly conducting periodical surveys, the closeness of the approaching election or the survey mode that is used. It can be observed that pollsters do not want to make predictions that vary greatly from the average of the other polls. Finally, editorial bias appears to play a significant role, especially in the case of traditional parties.

Los sondeos preelectorales constituyen una herramienta clave para que los ciudadanos comprendan el escenario electoral y evalúen qué partido o candidato tiene mayores posibilidades en cada convocatoria. Este artículo analiza si dichas encuestas generan un impacto diferencial entre los votantes indecisos y aquellos que ya han definido su elección. A través de una metodología cuantitativa, se ha estudiado la influencia del conocimiento de los datos demoscópicos en dos elecciones generales celebradas en España, utilizando como base las encuestas poselectorales del CIS correspondientes a abril y noviembre de 2019. Los resultados sugieren que los efectos varían en función del clima de opinión y del contexto de competencia partidista durante la campaña electoral. En términos generales, mientras que las encuestas tienden a reforzar las decisiones de los votantes ya determinados, para los indecisos representan una fuente de información adicional que puede ayudarlos a decidir su voto en el último momento.

The prediction of opinion distribution in real-world scenarios represents a major scientific challenge for current social networks analysis. In terms of electoral forecasting, we find several prediction solutions that try to combine statistics with economic indices, and machine learning, like multilevel regression and post-stratification (MRP). Nevertheless, recent studies pinpoint toward the importance of temporal characteristics in the diffusion of opinion. As such, we take inspiration from micro-scale temporal epidemic models and develop an original time-aware (TA) forecasting methodology which is able to improve the prediction of opinion distribution in an electoral context. After a parametric analysis, we validate our TA method with pre-election survey data from three presidential elections (2012–2019) and the UK Brexit (2016). Benchmarking our TA method against two classic statistical approaches, like survey averaging (SA), and cumulative vote counting (CC), and the best pollster predictions, we find that our method is substantially closer to the real election outcomes. On average, we measure prediction errors of 9.8% (SA), 9.6% (CC), 5.1% (MRP), and only 3.0% for TA; these differences translate into increases of prediction accuracy of ≈ 71 % for the TA method (40% better than best pollsters). Moreover, TA does not require socio-economical contextual information, while the more complex MRP method depends on them for prediction. This work builds upon existing studies on the microscopic temporal dynamics of social networks and offers new insight on how macroscopic prediction can be improved using time-awareness.

We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.