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Posted: 2016-04-20T22:32:41Z | Updated: 2016-04-21T21:42:29Z Voters Who Say They'd Never Support A Candidate Don't Necessarily Mean It | HuffPost

Voters Who Say They'd Never Support A Candidate Don't Necessarily Mean It

Voters tend to soften on their party's candidate when they view the other party's nominee as even worse.
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Judging by recent polls, Americans don't really want anyone for president.

Faced with a historically unpopular 2016 field, most voters can't see themselves supporting Donald Trump, Texas Sen. Ted Cruz or Hillary Clinton, according to a recent NBC/Wall Street Journal poll. Just under half also rule out Vermont Sen. Bernie Sanders and Ohio Gov. John Kasich.

As a follow-up question from the same survey shows, though, some of those anti-Clinton voters changed their minds when presented with Trump as the alternative.

"I'd never vote for them," in other words, often means, "unless the other guy would be even worse."

That's especially true among the members of each party, whose displeasure with the results of a primary election often dims by the time November rolls around.

A new HuffPost/YouGov poll , conducted before Tuesday's New York primary, shows partisanship is likely to remain a driving factor this year, even among independent voters who lean toward only one party. 

Seventy-eight percent of Democrats and Democratic-leaning independent voters agreed that "the worst Democratic candidate for president is still better than the Republican candidates"; 77 percent of Republican and Republican-leaning voters said "the worst Republican candidate for president is still better than the Democratic candidates." Just 2 percent of each party's voters planned to support a candidate for the rival party.

Despite what opponents of Clinton and Trump might want to believe, neither candidate is particularly unpalatable to their party, when compared with their rivals.

Just 15 percent of Democratic and Democratic-leaning voters said they couldn't support Clinton as the nominee, while an equal percentage couldn't see themselves backing Sanders. Sixty-two percent said they'd be able to support either.

The GOP is more divided, with fewer than a third saying they could support all three of their party's remaining candidates. But each contender drew about the same level of opposition. Twenty-six percent of registered Republicans and Republican leaners said they couldn't support Cruz, while 27 percent said the same of Kasich, and 28 percent of Trump.

Those numbers are likely, if anything, an overstatement on both sides. In 2008, exit polls showed that half of Clinton's supporters in key states like Indiana and North Carolina said that they wouldn't support Barack Obama in a general election. Most, apparently, got over it. Obama won 89 percent of his party in the general election.

Some disgruntled voters, of course, may just stay home. About one-quarter of voters in both parties said that if they couldn't bring themselves to support their party's nominee, they wouldn't vote. By November, though, the share of voters willing to take such a hard line may dwindle.

The HuffPost/YouGov poll consisted of 1,000 completed interviews conducted April 12 to April 14 among U.S. adults, using a sample selected from YouGov’s opt-in online panel to match the demographics and other characteristics of the adult U.S. population.

The Huffington Post has teamed up with YouGov to conduct daily opinion polls.You can learn more about this project and take part in YouGov’s nationally representative opinion polling. Data from all HuffPost/YouGov polls can be found here . More details on the polls’ methodology are available here .

Most surveys report a margin of error that represents some, but not all, potential survey errors. YouGov’s reports include a model-based margin of error, which rests on a specific set of statistical assumptions about the selected sample, rather than the standard methodology for random probability sampling. If these assumptions are wrong, the model-based margin of error may also be inaccurate. Click here for a more detailed explanation of the model-based margin of error.

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