Predicting the Likelihood to Vote in Pre-Election Polls

Abstract

A problem of particular interest in pre‐election polls is to predict the likelihood that a sampled individual, whether respondent or non‐respondent, will vote. This can be especially difficult in state and local elections since voter turnout is low compared to national elections. We study this problem by means of validated pre‐election polls of registered voters in a US city which were conducted for the 1988 Presidential and 1989 Virginia State Gubernatorial elections. Results indicate that respondents and 'refusals' are significantly more likely to vote than non‐respondents who cannot be contacted, and that a respondent's self‐described likelihood to vote is the best indicator that he will. A logit model is used to predict the likelihood that a registered voter will vote in the State election based on voting status in the Presidential election, age and sex. The presence of the variable sex in the model is explained by the high profile of the abortion issue in the campaign.

Comparison of the Likelihood to Vote Among Preelection Poll Respondents and Nonrespondents

“…As we show in the last paragraph, the main conclusions are: (a) there is no significant difference between the percentage of voters among respondents and interview refusals, (b) these two groups combined have a significantly higher percentage of voters than the call rule exhausted group, and (c) the interview refusal and call rule exhausted groups combined have a significantly higher percentage of voters than the unavailable group, although the fact that our preelection poll was conducted only 1 week prior to the election may account for some of this difference. These conclusions are supported in a subsequent study (Bolstein 1991).…”

“…Likewise, the difference between the percentage of voters among the unavailable and call rule exhausted groups is not significant. These nonsignificant pairwise differences may be due to small sample sizes since the trend is supported in a subsequent study (Bolstein 1991).…”

Calibrating Non-Probability Surveys to Estimated Control Totals Using LASSO, with An Application to Political Polling

“…The data were collected between October 3rd and November 4th, 2014 (the election day). Because conditioning on likely voters improves election prediction (Bolstein, ; Gutsche et al ., ), we restricted our analysis to those who indicated that they (a)had already voted, (b)were absolutely certain to vote or (c)were very likely to vote. …”

I’m Not Voting for Her: Polling Discrepancies and Female Candidates

“…This is why most pre-election polls are very successful in predicting the eventual winner (Bolstein 1991;NCPP 1997;Mitofsky 1998;Traugott 2005;Keeter and Samaranayake 2007). Several authors have shown that the best predictor of whether or not a person will vote and who they will vote for can be found in their pre-election poll response (Mosteller 1949;Bolstein 1991;Mitofsky 1998). Despite the general accuracy of modern pre-election polls, there have been numerous instances in which the actual results do not match their predictions (the 1948 and 1996 Presidential elections are often cited as examples of particularly poor poll performance).…”

“…With respect to other features of the election, we also considered whether the election had an uncharacteristically high (or low) turnout which could lead to less accurate polls (Crespi 1988;Bolstein 1991;Mitofsky 1998). This variable was 4 Unfortunately, we are not able to know whether polls were conducted using Interactive Voice Response (IVR) polls.…”