Brexit polling: What went wrong?

June 24, 2016
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Commenter numeric writes:

Since you were shilling for yougov the other day you might want to talk about their big miss on Brexit (off by 6% from their eve-of-election poll—remain up 2 on their last poll and leave up by 4 as of this posting).

Fair enough: Had Yougov done well, I could use them as an example of the success of MRP, and political polling more generally, so I should take the hit when they fail. It looks like Yougov was off by about 4 percentage points (or 8 percentage points if you want to measure things by vote differential). It will be interesting to how much this difference was nonuniform across demographic groups.

The difference between survey and election outcome can be broken down into five terms:

1. Survey respondents not being a representative sample of potential voters (for whatever reason, Remain voters being more reachable or more likely to respond to the poll, compared to Leave voters);

2. Survey responses being a poor measure of voting intentions (people saying Remain or Undecided even though it was likely they’d vote to leave);

3. Shift in attitudes during the last day;

4. Unpredicted patterns of voter turnout, with more voting than expected in areas and groups that were supporting Leave, and lower-than-expected turnout among Remain supporters.

5. And, of course, sampling variability. Here’s Yougov’s rolling average estimate from a couple days before the election:

freddie1

Added in response to comments: And here’s their final result, “YouGov on the day poll: Remain 52%, Leave 48%”:

Final poll

We’ll take this final 52-48 poll as Yougov’s estimate.

Each one of the above five explanations seems to be reasonable to consider as part of the story. Remember, we’re not trying to determine which of 1, 2, 3, 4, or 5 is “the” explanation; rather, we’re assuming that all five of these are happening. (Indeed, some of these could be happening but in the opposite direction; for example it’s possible that the polls oversampled Remain voters (a minus sign on item 1 above) but that this non-representativeness was more than overbalanced by a big shift in attitudes during the last day (a big plus sign on item 3).

The other thing is that item 5, sampling variability, does not stand on its own. Given the amount of polling on this issue (even within Yougov itself, as indicated by the graph above), sampling variability is an issue to the extent that items 1-4 above are problems. If there were no problems with representativeness, measurement, changes in attitudes, and turnout predictions, then the total sample size of all these polls would be enough that they’d predict the election outcome almost perfectly. But given all these other sources of uncertainty and variation, you need to worry about sampling variability too, to the extent that you’re using the latest poll to estimate the latest trends.

OK, with that as background, what does Yougov say? I went to their website and found this article posted a few hours ago:

Unexpectedly high turnout in Leave areas pushed the campaign to victory

Unfortunately YouGov was four points out in its final poll last night, but we should not be surprised that the referendum was close – we have shown it close all along. Over half our polls since the start of the year we showed Brexit in the lead or tied. . . .

As we wrote in the Times newspaper three days ago: “This campaign is not a “done deal”. The way the financial and betting markets have reacted you would think Remain had already won – yesterday’s one day rally in the pound was the biggest for seven years, and the odds of Brexit on Betfair hit 5-1. But it’s hard to justify those odds using the actual data…. The evidence suggests that we are in the final stages of a genuinely close and dynamic race.”

Just to check, what did Yougov say about this all before the election? Here’s their post from the other day, which I got by following the links from my post linked above:

Our current headline estimate of the result of the referendum is that Leave will win 51 per cent of the vote. This is close enough that we cannot be very confident of the election result: the model puts a 95% chance of a result between 48 and 53, although this only captures some forms of uncertainty.

The following three paragraphs are new, in response to comments, and replace one paragraph I had before:

OK, let’s do a quick calculation. Take their final estimate that Remain will win with 52% of the vote and give it a 95% interval with width 6 percentage points (a bit wider than the 5-percentage-point width reported above, but given that big swing, presumably we should increase the uncertainty a bit). So the interval is [49%, 55%], and if we want to call this a normal distribution with mean 52% and standard deviation 1.5%, then the probability of Remain under this model would be pnorm(52, 50, 1.5) = .91, that is, 10-1 odds in favor. So, when Yougov said the other day that “it’s hard to justify those [Betfair] odds” of 5-1, it appears that they (Yougov) would’ve been happy to give 10-1 odds.

But these odds are very sensitive to the point estimate (for example, pnorm(51.5, 50, 1.5) = .84, which gives you those 5-1 odds), to the forecast uncertainty (for example, pnorm(52, 50, 2.5) = .79), and to any smoothing you might do (for example, take a moving average of the final few days and you get something not far from 50/50).

In short, betting odds in this setting are highly sensitive to small changes in the model, and when the betting odds stay stable (as I think they were during the final period of Brexit), this suggests they contain a large element of convention or arbitrary mutual agreement.

The “out” here seems to be that last part of Yougov’s statement from the other day: “although this only captures some forms of uncertainty.”

It’s hard to know how to think about other forms of uncertainty, and I think that one way that people handle this in practice is to present 95% intervals and treat them as something more like 50% intervals.

Think about it. If you want to take the 95% interval as a Bayesian predictive interval—and Yougov does use Bayesian inference—then you’d be concluding that the odds are 40-1 that Remain would get more than 48% of the vote the outcome would fall below the lower endpoint of the interval. That’s pretty strong. But that would not be an appropriate conclusion to draw, not if you remember that this interval “only captures some forms of uncertainty.” So you can mentally adjust the interval, either by making it wider to account for these other sources of uncertainty, or by mentally lowering its probability coverage. I argue that in practice people do the latter, that they take 95% intervals as statements of uncertainty, without really believing the 95% part.

OK, fine, but if that’s right, then did the betting markets appear to be taking Yougov’s uncertainties literally with those 5-1 odds? There I’m guessing the problem was . . . other polls. Yougov was saying 51% for Leave, or maybe 52% for Remain, but other polls were showing large leads for Remain. If all the polls had looked like Yougov, and had betters been rational about accounting for nonsampling error, we might have seen something like 3-1 or 2-1 odds in favor, which would’ve been more reasonable (from a prospective sense, given Yougov’s pre-election polling results and our general knowledge that nonsampling error can be a big deal).

Houshmand Shirani-Mehr, David Rothschild, Sharad Goel, and I recently wrote a paper estimating the level of nonsampling error in U.S. election polls, and here’s what we found:

It is well known among both researchers and practitioners that election polls suffer from a variety of sampling and non-sampling errors, often collectively referred to as total survey error. However, reported margins of error typically only capture sampling variability, and in particular, generally ignore errors in defining the target population (e.g., errors due to uncertainty in who will vote). Here we empirically analyze 4,221 polls for 608 state-level presidential, senatorial, and gubernatorial elections between 1998 and 2014, all of which were conducted during the final three weeks of the campaigns. Comparing to the actual election outcomes, we find that average survey error as measured by root mean squared error (RMSE) is approximately 3.5%, corresponding to a 95% confidence interval of ±7%—twice the width of most reported intervals.

Got it? Take that Yougov pre-election 95% interval of [.48,.53] and double its width and you get something like [.46,.56] which more appropriately captures your uncertainty.

That all sounds just fine. But . . . I didn’t say this before the vote? So now the question is not, “Yougov: what went wrong?” or “UK bettors: what went wrong?” but, rather, “Gelman: what went wrong?”

That’s a question I should be able to answer! I think the most accurate response is that, like everyone else, I was focusing on the point estimate rather than the uncertainty. And, to the extent I was focusing on the uncertainty I was implicitly taking reported 95% intervals and treating them like 50% intervals. And, finally, I was probably showing too much deference to the betting line.

But I didn’t put this all together and note the inconsistency between the wide uncertainty intervals from the polls (after doing the right thing and widening the intervals to account for nonsampling errors) and the betting odds. In writing about the pre-election polls, I focused on the point estimate and didn’t focus in on the anomaly.

I should get some credit for attempting to untangle these threads now, but not as much as I’d deserve if I’d written this all two days ago. Credit to Yougov, then, for publicly questioning the 5-1 betting odds, before the voting began.

OK, now back to Yougov’s retrospective:

YouGov, like most other online pollsters, has said consistently it was a closer race than many others believed and so it has proved. While the betting markets assumed that Remain would prevail, throughout the campaign our research showed significantly larger levels of Euroscepticism than many other polling organisations. . . .

Early in the campaign, an analysis of the “true” state of public opinion claimed support for Leave was somewhere between phone and online methodologies but a little closer to phone. We disputed this at the time as we were sure our online samples were getting a much more representative sample of public opinion.

Fair enough. They’re gonna take the hit for being wrong, so they might as well grab what credit they can for being less wrong than many other pollsters. Remember, there still are people out there saying that you can’t trust online polls.

And now Yougov gets to the meat of the question:

We do not hide from the fact that YouGov’s final poll miscalculated the result by four points. This seems in a large part due to turnout – something that we have said all along would be crucial to the outcome of such a finely balanced race. Our turnout model was based, in part, on whether respondents had voted at the last general election and a turnout level above that of general elections upset the model, particularly in the North.

So they go with explanation 4 above: unexpected patterns of turnout.

They frame this as a North/South divide—which I guess is what you can learn from the data—but I’m wondering if it’s more of a simple Leave/Remain divide, with Leave voters being, on balance, more enthusiastic, hence turning out to vote at a higher-than-expected rate.

Related to this is explanation 3, changes in opinion. After all, that Yougov report also says, “three of YouGov’s final six polls of the campaign showing ‘Leave’ with the edge ranging from a 4% Remain lead to an 8% Leave lead.” And if you look at the graph reproduced above, and take a simple average, you’ll see a win for Leave. So the only way to call the polls as a lead for Remain (as Yougov did, in advance of the election) was to weight the more recent polls higher, that is to account for trends in opinion. It makes sense to account for trends, but once you do that, you have to accept the possibility of additional changes after the polling is done.

And, just to be clear: Yougov’s estimates using MRP were not bad at all. But this did not stop Yougov from reporting, as a final result, that mistaken 52-48 pro-Remain poll on the eve of the vote.

To get another perspective on what went wrong with the polling, I went to the webpage of Nikos Askitas, whose work I’d “shilled” on the sister blog the other day. Askitas had used a tally based on Google search queries—a method that he reported had worked for recent referenda in Ireland and Greece—and reported just before the election a slight lead for Remain, very close to the Yougov poll, as a matter of fact. Really kind of amazing it was so close, but I don’t know what adjustments he did to the data to get there; it might well be that he was to some extent anchoring his estimates to the polls. (He did not preregister his data-processing rules before the campaign began.)

Anyway, Askitas was another pundit to get things wrong. Here’s what he wrote in the aftermath:

Two ways ago observing the rate at which the brexit side was recovering from the murder of Jo Cox I was writing that “as of 16:15 hrs on Tuesday afternoon the leave searches caught up by half a percentage point going from 47% to 47.5%. If trend continues they will be at 53% or Thursday morning”. This was simply regressing the leave searches on each hours passed. When I then saw the first slow down I had thought that it might become 51% or 52% but recovering most of the pre-murder momentum was still possible with only one obstacle in its way: time. When the rate of recovery of the leave searches slowed down in the evening of the 22nd of June and did not move upwards in the early morning of the 23rd I had to call the presumed trend as complete: if your instrument does not pick up measurement variation then you declare the process you are observing for finished. Leave was at 48%.

What explains the difference? Maybe the trend I was seeing early on was indeed still mostly there and there was simply no time to be recorded in search? Maybe the rain damaged the remaineers as it is widely believed? Maybe the pour turnout in Wales? Maybe our tool does not have the resolution it needs for such a close call? or maybe as I was saying elsewhere “I am confident to mostly have identified the referendum relevant searches and I can see that many -but not all- of the top searches are indeed related to voting intent”.

Askitas seems to be focusing more on items 2 and 3 (measurement issues and opinion changes) and not so much on item 1 (non-representativeness of searchers) and item 4 (turnout). Again, let me emphasize the that all four items interact.

Askitas also gives his take on the political outcome:

The principle of parliamentary sovereignty implies that referendum results are not legally binding and that action occurs at the discretion of the parliament alone. Consequently a leave vote is not identical with leaving. As I was writing elsewhere voting leave is hence cheap talk and hence the rational thing to do: you can air any and all grievances with the status quo and it is your vote if you have any kind of ax to grind (and most people do). Why wouldn’t you want to do so? The politicians can still sort it out afterwards. These politicians are now going to have to change their and our ways. Pro European forces in the UK, in Brussels and other European capitals must realize that scaremongering is not enough to stir people towards Europe. We saw that more than half of the Britons prefer a highly uncertain path than the certainty of staying, a sad evaluation of the European path. Pro Europeans need to paint a positive picture of staying instead of ugly pictures of leaving and most importantly they need to sculpt it in 3D reality one European citizen at a time.

P.S. I could’ve just as well titled this, “Brexit prediction markets: What went wrong?” But it seems pretty clear that the prediction markets were following the polls.

P.P.S. Full disclosure: YouGov gives some financial support to the Stan project. (I’d put this in my previous post on Yougov but I suppose the commenter is right that I should add this disclaimer to every post that mentions the pollster. But does this mean I also need to disclose our Google support every time I mention googling something? And must I disclose my consulting for Microsoft ever time I mention Clippy? I think I’ll put together a single page listing outside support and then I can use a generic disclaimer for all my posts.

P.P.P.S. Ben Lauderdale sent me a note arguing that Yougov didn’t do so bad at all:

I worked with Doug Rivers on the MRP estimates you discussed in your post today. I want to make an important point of clarification: none of the YouGov UK polling releases *except* the one you linked to a few days back used the MRP model. All the others were 1 or 2 day samples adjusted with raking and techniques like that. The MRP estimates never showed Remain ahead, although they got down to Leave 50.1 the day before the referendum (which I tweeted). The last run I did the morning of the referendum with the final overnight data had Leave at 50.6, versus a result of Leave 51.9.

Doug and I are going to post a more detailed post-mortem on the estimates when we recover from being up all night, but fundamentally they were a success: both in terms of getting close to the right result in a very close vote, and also in predicting the local authority level results very well. Whether our communications were successful is another matter, but it was a very busy week in the run up to the referendum, and we did try very hard to be clear about the ways we could be wrong in that article!

P.P.P.P.S. And Yair writes:

I like the discussion about turnout and Leave voters being more enthusiastic. My experience has been that it’s very difficult to separate turnout from support changes. I bet if you look at nearly any stable subgroup (defined by geography and/or demographics), you’ll tend to see the two moving together.

Another piece here, which I might have missed in the discussion, is differential non-response due to the Jo Cox murder. Admittedly I didn’t follow too closely, but it seems like all the news covereage in recent days was about that. Certainly plausible that this led to some level of Leave non-response, contributing to the polling trend line dipping towards Remain in recent days. I don’t think the original post mentioned fitting the MRP with Party ID (or is it called something else in the UK?), but I’m remembering the main graphs from the swing voter Xbox paper being pretty compelling on this point.

Last — even if the topline is off, in my view there’s still a lot of value in getting the subgroups right. I know I was informally looking at the YouGov map compared to the results map last night. Maybe would be good to see a scatterplot or something. I know everyone cares about the topline more than anything, but to me (and others, I hope) the subgroups are important, both for understanding the election and for understanding where the polls were off.

The post Brexit polling: What went wrong? appeared first on Statistical Modeling, Causal Inference, and Social Science.



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