(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Alan Abramowitz sent us the above graph, which shows the results from a series of recent national polls, for each plotting Hillary Clinton’s margin in support (that is, Clinton minus Trump in the vote-intention question) vs. the Democratic Party’s advantage in party identification (that is, percentage Democrat minus percentage Republican).
This is about as clear a pattern as you’ll ever see in social science: Swings in the polls are driven by swings in differential nonresponse. After the Republican convention, Trump supporters were stoked, and they were more likely to respond to surveys. After the Democratic convention, the reverse: Democrats are more likely to respond, driving Clinton up in the polls.
David Rothschild and I have the full story up at Slate:
Tou sort of know there is a convention bounce that you should sort of ignore, but why? What’s actually in a polling bump? The recent Republican National Convention featured conflict and controversy and one very dark acceptance speech—enlivened by some D-list celebrities (welcome back Chachi!)—but it was still enough to give nominee Donald Trump a big, if temporary, boost in many polls. This swing, which occurs predictably in election after election, is typically attributed to the persuasive power of the convention, with displays of party unity persuading partisans to vote for their candidate and cross-party appeals coaxing over independents and voters of the other party.
Recent research, however, suggests that swings in the polls can often be attributed not to changes in voter intention but in changing patterns of survey nonresponse: What seems like a big change in public opinion turns out to be little more than changes in the inclinations of Democrats and Republicans to respond to polls. We learned this from a study we performed [with Sharad Goel and Wei Wang] during the 2012 election campaign using surveys conducted on the Microsoft Xbox. . . .
Our Xbox study showed that very few respondents were changing their vote preferences—less than 2 percent during the final month of the campaign—and that most, fully two-thirds, of the apparent swings in the polls (for example, a big surge for Mitt Romney after the first debate) were explainable by swings in the percentages of Democrats and Republicans responding to the poll. This nonresponse is very loosely correlated with likeliness to vote but mainly reflects passing inclinations to participate in polling. . . . large and systematic changes in nonresponse had the effect of amplifying small changes in actual voter intention. . . .
[See this paper, also with Doug Rivers, with more, including supporting information from other polls.]
We can apply these insights to the 2016 convention bounces. For example, Reuters/Ipsos showed a swing from a 15-point Clinton lead on July 14 to a 2-point Trump lead on July 27. Who was responding in these polls? The pre-convention survey saw 53 percent Democrats, 38 percent Republican, and the rest independent or supporters of other parties. The post-convention respondents looked much different, at 46 percent Democrat, 43 percent Republican. The 17-point swing in the horse-race gap came with a 12-point swing in party identification. Party identification is very stable, and there is no reason to expect any real swings during that period; thus, it seems that about two-thirds of the Clinton-Trump swing in the polls comes from changes in response rates. . . .
Read the whole thing.
The political junkies among you have probably been seeing all sorts of graphs online showing polls and forecasts jumping up and down. These calculations typically don’t adjust for party identification (an idea we wrote about back in 2001, but without realizing the political implications that come from systematic, rather than random, variation in nonresponse) and thus can vastly overestimate swings in preferences.
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