The Elusive Likely Voter
Bibliographic Data
| ID | 6370205 |
|---|---|
| Authors | Anthony Rentsch (Institute for Applied Computational Science at Harvard University, Cambridge, MA, USA), Brian F Schaffner, Bertram Schaffner (0000-0003-1953-2464, Department of Political Science and Tisch College at Tufts University, Medford, MA, USA, corresponding author), Justin H Gro (0000-0001-8997-7691), Justin H Gross (University of Massachusetts Amherst, Amherst, MA, USA) |
| Year | 2019 |
| Volume | 83 |
| Issue | 4 |
| Pages | 782-804 |
| Publication date | 2019-12-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Public Opinion Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 0033-362X • E-ISSN: 1537-5331 |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1093/poq/nfz052 |
| OpenAlex | W3004054083 |
| Language | EN |
| Citations received | 5 |
| References cited | 18 |
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 preelection 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 study, we use Cooperative Congressional Election Study surveys since 2008 to build a probabilistic likely-voter model that takes into account not only the stated intentions of respondents to vote, but also other demographic variables that are consistently strong predictors of both turnout and overreporting. This model, which we term the Perry-Gallup and Demographics (PGaD) approach, shows that the bias and error created by likely-voter models can be reduced 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
Demographics · Econometrics · Economics · Political science · Politics · Polling · Probabilistic logic · Sociology · Statistics · Survey data collection · Term (time · Voter model · Voter registration · Voter turnout · Voting · Voting behavior · Weighting · Advanced Causal Inference Techniques · Computer Science · Electoral Systems and Political Participation · Game Theory and Voting Systems · Mathematics · Psychology · Artificial Intelligence · Demography · Social Psychology
Participation in America
Who Votes Now?
A Review and Proposal for a New Measure of Poll Accuracy
Building a Probable Electorate From Preelection Polls
Measuring Voter Registration and Turnout in Surveys
An Evaluation of the 2016 Election Polls in the United States
Validating Self-Reported Turnout by Linking Public Opinion Surveys with Administrative Records
Pre-Election Polling
Overreporting Voting
Likely (and Unlikely) Voters and the Assessment of Campaign Dynamics
Why Does the American National Election Study Overestimate Voter Turnout
Estimating Smooth Country–Year Panels of Public Opinion
Validation
What Affects Voter Turnout
Vote Self-Prediction Hardly Predicts Who Will Vote, and Is (Misleadingly) Unbiased
The perils of cherry picking low frequency events in large sample surveys
Legislative Staff and Representation in Congress
| Unique citing works | 5 |
|---|---|
| Citations per year | 1,25 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |