How do pollsters model for “likely voters”? That question takes on larger importance this year, as the COVID-19 pandemic, riots, and widely divergent campaign styles of the two presidential contenders raise questions about turnout impact. Polls based on responses of “likely voters” could be less predictive than in previous cycles if pollsters can’t figure out how to factor all of those issues into their models.
Or perhaps this year won’t be any different, as Mark Mellman argued yesterday at The Hill. That’s not because pollsters can handle the adaptations needed, the Democratic pollster writes. It’s because likely-voter screens and models are a “sham” already:
Last year, political scientists Anthony Rentsch, Brian Schaffner and Justin Gross analyzed the 64,600 interviews from the 2016 Cooperative Congressional Election study and found just 64 percent of those who maintained they were “definitely” going to vote did so. Only 68 percent of those who claimed they had already voted actually cast a ballot at any point. Meanwhile, 18 percent of self-reported “less likely” did turn out, as did 9 percent of those who claimed they had no intention of voting.
That question may be particularly blunt, but more complex items haven’t proved superior.
Among those who scored the highest on Gallup’s multi-item scale in 2014, 83 percent voted, but 17 percent didn’t. Moreover, more than 20 percent of those who did cast ballots scored at the bottom end of the scale and would have been removed from the “likely voter” pool.
What about surrogates like “enthusiasm?”
In one race, we found among those “very” enthusiastic, 88 percent voted; among those who were “somewhat” enthusiastic, 83 percent turned out; and among those “not too” enthusiastic, a slightly larger 85 percent turned out. No relationship.