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"The Elusive Likely Voter: Improving Electoral Predictions by Modeling Vote Propensity" co-authored by Justin Gross, Brian Schaffner & Anthony Rentsch

Anthony Rentsch, Political Science B.A. 2018, is lead author on this article that was just accepted for publication at Public Opinion Quarterly. The article, co-authored by Brian F. Schaffner and Justin H. Gross, grew out of Anthony's honors thesis in the Department of Political Science. (The link will be shared once it is available online).

Political commentators have offered evidence that the “polling misses” of 2016 were caused by a number of factors. This project focuses on one explanation, that likely voter models – tools used by pre-election pollsters to predict which survey respondents are most likely to make up the electorate and, thus, whose responses should be used to calculate election predictions – were flawed. While models employed by different pollsters vary widely, it is difficult to systematically study them because they are often considered part of pollsters’ methodological black box. In this paper, we use Cooperative Congressional Election Study surveys since 2008 to build a probabilistic likely voter model that not only takes into account the stated intentions of respondents to vote, but also other demographic variables that are consistently strong predictors of both turnout and over-reporting. Using this model, which we term the Perry-Gallup and demographics (PGaD) approach, we show that we are able to reduce the bias created by likely voters model to a negligible amount. This likely voter approach uses variables that pollsters already collect for weighting purposes and thus should be relatively easy to implement in future elections.

Political commentators have offered evidence that the “polling misses” of 2016 were caused by a number of factors. This project focuses on one explanation, that likely voter models – tools used by pre-election pollsters to predict which survey respondents are most likely to make up the electorate and, thus, whose responses should be used to calculate election predictions – were flawed. While models employed by different pollsters vary widely, it is difficult to systematically study them because they are often considered part of pollsters’ methodological black box. In this paper, we use Cooperative Congressional Election Study surveys since 2008 to build a probabilistic likely voter model that not only takes into account the stated intentions of respondents to vote, but also other demographic variables that are consistently strong predictors of both turnout and over-reporting. Using this model, which we term the Perry-Gallup and demographics (PGaD) approach, we show that we are able to reduce the bias created by likely voters model to a negligible amount. This likely voter approach uses variables that pollsters already collect for weighting purposes and thus should be relatively easy to implement in future elections.

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