Which countries have Regrexit?

This doesn't have a lot to do with bio part of biostatistics, but is an interesting data analysis that I just started. In the wake of the Brexit vote, there is a petition for a redo. The data for the petition is here, in JSON format.

Fortunately, in R, working with JSON data is pretty easy. You can easily download the data from the link and put it into a data frame. I start on that here, with the RJSONIO package, ggplot2, and a version of the petition I downloaded on 6/26/16.

One question I had was whether all the signers are British. Fortunately, the petition collects the place of residence of the signer, assuming no fraud. I came up with the following top 9 non-UK countries of origin of signers.

There are a couple of things to remember when interpreting this graph:

1. I left off the UK. The number of signatures is over 3 million, and contains by far the largest percentage of signatories.
2. 5 of the 9 top countries are neighbors, including the top 2. The other 4 are Australia, the US, Canada, and New Zealand, who are all countries that have strong ties to the UK.
3. This assumes no petition fraud, which I can't guarantee. I saw at least one Twitter posting telling people to use her (if the profile pic is to be believed) residence code. There is a section of the petition data showing constituency, so I'm wondering if it would be possible to analyze the petition for fraud. I'm not as familiar with British census data as I am with US, but I imagine a mashup of the two would be useful.

(Update: Greg Jefferis posted a nice analysis here. See comments below.)

Sloppy journalism with interactive graphics is still sloppy journalism

The Guardian recently discussed the "declining linguistic standards" in State of the Union addresses. I thought  this was an interesting exercise, but something seemed wrong about the article, and it turns out this is one case where the data do not really speak for themselves. There's a lot of interpretation and understanding behind cultural trends in the use of the English language in America, as well as the evolution of the presidents' intentions behind the address. There are a few important points:

• The author correctly points out that Woodrow Wilson essentially changed the format of the address through precedent from written document to speech. Right after Wilson's first speech there is a huge drop in the "education level" (hang on for a discussion of this terminology) of these addresses. As I recall, Wilson is the only American president with a Ph.D.
• The index used - Flesch-Kincaid (FK), is questionable. Good on The Guardian to use a single measure for all speeches, but I have to wonder if it is wise to use the same measure for speeches and written addresses. Furthermore, FK is very sensitive to the placement of punctuation (it weights sentence length heavily). For instance, as a friend pointed out, one of Wilson's speeches has a FK grade level of over 17, but if you replace one of the semi-colons in the speech with a period, the FK grade drops to 12. This subtlety is lost in speech format, giving FK an extremely high uncertainty (this same friend calls FK "utterly useless" for speeches).
• The audience of the SOTU address has changed. Though it's a constitutional duty of the president, the delivery as a speech is not, and it only has to be delivered to Congress. However, most modern addresses have been in the form of televised speeches, and have to be understood by a wider and less politically savvy audience.
• Cultural trends in the use of spoken and written English in America involve shorter sentences over time in general.
• In this case, a more sophisticated natural language processing analysis might reveal some interesting trends. For instance, how do wartime speeches compare to times of peace? Are there any natural categories of speeches that fall out? What are the outliers? How does this compare to polls?

In short, we have some interesting data that needs heavy qualification and critical analysis, that is just presented on a page and capped with a headline that gives an overly simplistic interpretation.

Politics vs. science and the Nate Silver controversy

I’ll take a small departure from the narrow world of biostatistics and comment on a wider matter.

Nate Silver of FiveThirtyEight has really kicked the hornet’s nest. This is a nest that really needed stirring, but I do not envy him for being the focus of attention.

This all started, I think, when he released his book and basically called political pundits out for a business model of generating drama rather than making good predictions. This wouldn’t be a huge deal, except that he has developed a statistical model that combines data from national and state polls with demographic data to project outcomes of presidential and senatorial elections. This model, as of this writing, has President Obama at close to an 81% probability of re-election, given the current state of things. As it turns out, there are a lot of people that don’t like this, and they generally fall into two camps:

1. People who would rather see President Obama defeated in the election, and

2. Pundits who have a vested interest in a dramatic “horse-race” election

I’ll add a third:

3. Pundits who want to remain relevant (whether to keep their jobs or reputations).

Frankly, I don’t think that pundits will have to worry about #3. There’s an allergy to fact in this country, a large group of people who would rather ignore established fact and cling to a fantasy. (You can find a sampling of these people over at the intelligent design blogosphere, for instance.) I think the demand for compelling stories over dry facts will remain.

I’ve run into people of the first type, when I’ve published some armchair statistician analyses based on Twitter sentiment, for instance. The responses weren’t critiques of the method, but rather, “who cares, Republicans rule!” Even more dangerous, I’ve run into similar responses to negative clinical study results in cases where sponsors have a vested interest in positive outcomes. (There was at least one case I remember a sponsor moved forward with an expensive study to follow on, and some where I was asked to reanalyze a zillion times.)

Nate write The Signal and the Noise where he, among a lot of explanation, points out that there is a whole cottage industry of people getting paid to BS about politics. So I think that some in the second category are starting to face an existential crisis, and that makes them dangerous.

Ultimately, we have to understand where Nate is coming from to understand his prediction. His money is (literally – He made a bet[1] on Twitter with “Morning Joe” Scarborough of NBC) on Obama’s victory in the election, not necessarily because he wants Obama to win, but because he has confidence in his prediction. When he made the bet, he made the controversy more than just trading words, but he called Joe’s bluff (Joe had said that anyone not calling the race a tossup is an ideologue). We can now call him The Statistician Who Kicked the Hornet’s Nest – the punditry, including the public editor of the New York Times that hosts his blog, is collectively attacking him.

Unfortunately, the punditry has the upper hand, because people are more interested in the narrative than the science.

[1] The bet originally consisted of the loser donating $1000 to charity. Nate subsequently donated $2538 to the Red Cross before the election.

Nate Silver on The Daily Show

Watch it!

There’s an interesting conversation about how the campaigns use analytics in get out the vote efforts. It doesn’t go a lot in depth, but I think this is an important aspect of campaigns that will come out into public view in the next couple of election cycles.

Of course, you can find his blog at http://fivethirtyeight.blogs.nytimes.com/.

More amateur polling analysis

So I follow up to a previous post exploring polling data by adding a couple of graphs with updated data. The underlying idea is the same - use LOESS to smooth the polls, don't mess with weighting them by quality, etc. I get the graph at the right for the raw polls. There are a couple of things I notice here: the distance between the predicted Obama poll result and 50% is less than the difference between the Obama result and the McCain result. Of course, the smoothing span in the loess might change that (I used loess(...,span=0.2) for this result). However, third party candidates do have the ability to confound matters, and what is more important in terms of who will be taking the inauguration speech on Jan 20 is the state-by-state polling. However, given that the 95% prediction intervals (given by the shading) have separated the McCain story at the state-by-state level is the same as that shown in the graph.

The difference graph is a little bit clearer (partly because it shows fewer data points). Other than a brief dip below 0 due to the rather sharp Republican convention bounce, Obama's been polling very strongly. These results are very similar to what you would find on Nate Silver's fivethirtyeight.com (where he not only uses the loess but also weights pollsters by quality based on previous elections, newness, and other factors). The 95% interval (light gray) has been clear over 0 for a week and a half (the dark gray is a 50% interval, just to get an idea of the tightness of the difference prediction).

If Nate Silver's analysis of the electoral college is correct (that Obama could go to -2.2 in the difference graph and still come out with an electoral college victory), the only time he was in trouble was in the week after the Republican convention.

Followon to polling post

Seems like the guy at fivethirtyeight.com is pretty on top of polling methods, including modeling of the polling data (weighting by past performance, for example). And he uses good methods at the back end, too.

A distraction into the world of polling data

I break from biostatistics for a bit to go into politics, specifically the tracking of polls. Polls are very noisy, and it's really hard to discern real trends (such as convention bounces or even long-term trends toward/away from candidates. The real hard statistical work seems to be in survey selection and sampling, but then on the backend in the reporting not much more is done. Unless you're these guys. So I tried my hand at it a little bit, just being an amateur with a PhD. I collected some poll data and tried my hand a using a LOESS rather than a 3 day moving average. I got the graph on the right.

It's notable that the one point seems to be an outlier (I think that is the Gallup poll that is being criticized in the left-leaning blogs), and McCain's bounce is very noticable, but certainly more data will be needed to show the size of the bounce, as LOESS is susceptible to boundary effects. I do like the fact that LOESS has a longer memory than a moving average and can make the "memory" fade over time rather than either consider it or not. I really wonder what's going to happen to McCain's huge "bounce" with next week's data.