2015
DOI: 10.1016/j.ijforecast.2014.06.001
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Forecasting elections with non-representative polls

Abstract: a b s t r a c tElection forecasts have traditionally been based on representative polls, in which randomly sampled individuals are asked who they intend to vote for. While representative polling has historically proven to be quite effective, it comes at considerable costs of time and money. Moreover, as response rates have declined over the past several decades, the statistical benefits of representative sampling have diminished. In this paper, we show that, with proper statistical adjustment, non-representati… Show more

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Cited by 322 publications
(268 citation statements)
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“…This method has already been used in other fields but, to the authors' knowledge, has so far not been applied to mass valuation. Hierarchical linear models have been successfully used since the 80s in the fields of education (Aitkin et al 1981;Raudenbush and Bryck 1986;Singh 2014), public policy (Duncan et al 1993;Tso and Guan 2014), criminology (Gelman 2007;Fagan et al 2015), and politics (Wang et al 2015). This method overcomes some limitations of the traditional regression models, which are based on the hypothesis that the individuals in the sample are independent, however this assumption is not always correct.…”
Section: Introductionmentioning
confidence: 99%
“…This method has already been used in other fields but, to the authors' knowledge, has so far not been applied to mass valuation. Hierarchical linear models have been successfully used since the 80s in the fields of education (Aitkin et al 1981;Raudenbush and Bryck 1986;Singh 2014), public policy (Duncan et al 1993;Tso and Guan 2014), criminology (Gelman 2007;Fagan et al 2015), and politics (Wang et al 2015). This method overcomes some limitations of the traditional regression models, which are based on the hypothesis that the individuals in the sample are independent, however this assumption is not always correct.…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, IDS and big data should be treated as non-probability samples, whose representativeness is hard to measure. Recently Wanga, Rothschildb, Goelb, and Gelman (2014) proposed Bayesian model-based estimation and poststratification that could be one of the possible approaches to the problem. Nonetheless, new data sources open up possibilities of extending the set of statistical sources, which should not be neglected.…”
Section: Summary and Discussionmentioning
confidence: 99%
“…This methodology has already been used in other fields like education (Singh, 2014), public policy (Tso & Guan, 2014), politics (Wang, Rothschild, Goel & Gelman, 2014) or criminology (Fagan, Wright & Pinchevsky, 2015).…”
Section: Proposed Methodology: the Hierarchical Linear Modelmentioning
confidence: 99%