**** Elections thread *****

[Poopadoop]

Special elections since Trump:

Kansas: 2016 won by R by 31 points. 2017: Won by R by 7 points. Swing +24% D.
Montana : 2016 won by R by 15 points. 2017: Won by R by 6 points. Swing +9% D.
California: only D candidates ran in 2017 S.E.
Georgia: 2016 won by R by 23 points. 2017: Won by R by 3 points. Swing +20% D.
SC: 2016 won by R 21 points. 2017: Won by R by 3 points. Swing +18% D.
Utah: 2016 won by R 47 points. 2017: Won by R 32 points. Swing +15% D.
PA: 2016 won by R 20 points. 2017: tossup (0 points). Swing +20% D.

See a pattern there?

[BananaStand]

See a pattern there?

Bad science. Go do the same analysis of special elections during the first years of the Reagan, Clinton, Bush 2, and Obama administrations then tell me if anything in your previous two posts amounts to a hill of dogshit

[wufwugy]

Special elections since Trump:

Kansas: 2016 won by R by 31 points. 2017: Won by R by 7 points. Swing +24% D.
Montana : 2016 won by R by 15 points. 2017: Won by R by 6 points. Swing +9% D.
California: only D candidates ran in 2017 S.E.
Georgia: 2016 won by R by 23 points. 2017: Won by R by 3 points. Swing +20% D.
SC: 2016 won by R 21 points. 2017: Won by R by 3 points. Swing +18% D.
Utah: 2016 won by R 47 points. 2017: Won by R 32 points. Swing +15% D.
PA: 2016 won by R 20 points. 2017: tossup (0 points). Swing +20% D.

See a pattern there?

I see a pattern. One that seems to be univariate in a multivariate world, one that probably has some real heteroskedasticity problems, and other statistical jargon I don't know about.

Let's analyze the ongoing PA election.

It has a 20% swing from R to D from 2016 when voting Trump to 2018 and Trump is not on the ballot. The district has a 50k net of D voters normally. It's in a district that won't exist in a few months. The previous R congressman in the district was humiliated and shamed over an affair and (reportedly) attempted abortion. The R in the current race has little personal appeal and poor fundraising. The D in the race ran as an R, specifically as a very Trumpian R.

The 20% swing data by itself tells the wrong story about what actually happened here.

[Poopadoop]

I see a pattern.

Me too.

One that seems to be univariate in a multivariate world,

The numbers are a representation of the outcome of the multivariate world. The numbers themselves are 'univariate' because that's the easiest way to summarize the net result of multiple effects. E.g., an average has a simple interpretation, the various numbers that go into the average have no simple interpretation in and of themselves, except as part of a summary figure such as a mean, mode, or standard deviation (among other things one might compute). But you are right to be skeptical of the summary statistics, because they often don't tell the whole story.

one that probably has some real heteroskedasticity problems

Heretoscedasticity refers to variation in an outcome variable changing along the dimension of a predictor variable. This is not relevant here since there is no variance in this particular predictor variable.

and other statistical jargon I don't know about..

Any analysis has assumptions that if not met, can negatively impact the reliability of the analysis. In the case of percentage data, one could test these six 'swing' values in a number of ways, and compute the relative likelihood that they came from a world in which Ds had gained popularity as indexed by these six special elections relative to the same elections held in 2016, to a world in which no change in popularity had occurred.

The easiest test is a simple binomial test, the only assumption of which is that the data are dichotomous (i.e., either x happens or y happens, not both). In this case, all the data show a change from 2016 to 2017, so that assumption is met. The data don't have to be normally distributed and even Taleb couldn't bitch at this test because the tails can be as fat as you like.

The binomial test can be applied to compare two models of the data. First, if no change has occurred, the swing in each election should be equally likely to favour either side. This is termed the 'null' hypothesis (Ho), and we can express this as p(R) = 0.5 and p(D) = 0.5, where p(R) and p(D) represent the probability of a swing favouring the Rs or Ds, respectively. Alternatively, if the swings were influenced by a general increase in D popularity that had happened between 2016 and 2017 in these districts, the predicted results will differ from the null, and we can call this the 'change' hypothesis (Hc)

The actual outcome was p(D) = 1.0 and p(R) = 0, since D support went up in 6/6 districts. One approach then is to base our change hypothesis on the maximum likelihood estimate for the data, which is essentially a model that assumes the real world is most likely to match the outcome (6/6 D swings or p(D) = 1 and p(R) = 0) rather than any other hypothetical situation which we did not observe (such as p(D) = .5 and p(R) = .5, as assumed by the null hypothesis).

You can compute the relative likelihood of 6/6 D swings happening by computing the relative probability of that event given either model and dividing, then applying a penalty for the free parameter in the change Hypothesis.

p(6/6|Ho) = .015625

p(6/6|Hc) = 1

LR Hc:Ho = 1/.015625 * 1/exp(1)

= 23.5

So the data are 23.5 times as likely to occur if Dems were performing better in all six districts in 2017 relative to 2016 than if there was no difference.

Let's analyze the ongoing PA election.

It has a 20% swing from R to D from 2016 when voting Trump to 2018 and Trump is not on the ballot. The district has a 50k net of D voters normally. It's in a district that won't exist in a few months. The previous R congressman in the district was humiliated and shamed over an affair and (reportedly) attempted abortion. The R in the current race has little personal appeal and poor fundraising. The D in the race ran as an R, specifically as a very Trumpian R.

The 20% swing data by itself tells the wrong story about what actually happened here.

Not quite. You are ascribing variables to the outcome of the election that while you may find them plausible, have effects that have not and cannot be measured. My analysis is simply that a change occurred (or is very likely to have occurred, 23.5:1). I am analyzing the data objectively, you are explaining them subjectively. In other words, I am saying what happened, and you are trying to explain why it happened.

There is nothing wrong with subjective analyses, as long as you acknowledge they are subjective.

[BananaStand]

I am analyzing the data objectively, you are explaining them subjectively. In other words, I am saying what happened, and you are trying to explain why it happened.

WRONG

You started this whole conversation by asserting that the 26% swing was ascribable, specifically, to distaste for Trump.

You started with a "why". Now that its debunked, you've retreated to a very narrow island of "what"

Lame

[Poopadoop]

WRONG

You started this whole conversation by asserting that the 26% swing was ascribable, specifically, to distaste for Trump.

You started with a "why". Now that its debunked, you've retreated to a very narrow island of "what"

Lame

Nope, I gave a figure and offered an explanation. You started out by trying to dispute the figure, and got spanked. Now you've given up on that and want to argue the explanation has been 'debunked'. My argument is the figure is what the figure is; you can explain it a different way than I do but that doesn't prove anything, nor does it change the figure itself.

[wufwugy]

Heretoscedasticity refers to variation in an outcome variable changing along the dimension of a predictor variable. This is not relevant here since there is no variance in this particular predictor variable.

Is the D changing over the sample not heteroskedastic?

Not quite. You are ascribing variables to the outcome of the election that while you may find them plausible, have effects that have not and cannot be measured. My analysis is simply that a change occurred (or is very likely to have occurred, 23.5:1). I am analyzing the data objectively, you are explaining them subjectively. In other words, I am saying what happened, and you are trying to explain why it happened.

There is nothing wrong with subjective analyses, as long as you acknowledge they are subjective.

That's fine. Per the definitions you use, the objective analysis, while perhaps objective, is insufficient enough as to not provide much confidence in the conclusion.

I'm not attempting to explain that I understand what conclusion the data converges on, though I am saying that if the claim is that if there was a swing away from Trump because there was an R to D swing yet the D in this swing ran as a very Trumpian R, the data showing the swing is ill used when used to claim there was a swing away from Trump and/or R.

[Poopadoop]

Is the D changing over the sample not heteroskedastic?

The D swings are the outcome variable, the predictor variable is 2016 vs. 2017. If, say, you measured the swings across a number of years, say 2008, 2010...2016, and the variability in the swings differed depending on which two years you were comparing (i.e., which two years you examined a number of swings over) that would be an example of heteroscedacity.

That's fine. Per the definitions you use, the objective analysis, while perhaps objective, is insufficient enough as to not provide much confidence in the conclusion.

That's your subjective opinion.

I'm not attempting to explain that I understand what conclusion the data converges on, though I am saying that if the claim is that if there was a swing away from Trump because there was an R to D swing yet the D in this swing ran as a very Trumpian R, the data showing the swing is ill used when used to claim there was a swing away from Trump and/or R.

The claim though is a bit more general than this particular special election in PA. To 'debunk' it you would typically be expected to provide an alternative explanation for every one of those 6 swings. Arguably, these would become more and more complicated until your model includes several extra variables to explain each case. My model only has one variable, Trump. Doesn't mean the more complex model can't be true, but in scientific inference generally the simpler model is preferred.

[wufwugy]

The D swings are the outcome variable, the predictor variable is 2016 vs. 2017. If, say, you measured the swings across a number of years, say 2008, 2010...2016, and the variability in the swings differed depending on which two years you were comparing (i.e., which two years you examined a number of swings over) that would be an example of heteroscedacity.

I'm referring to the change in the D candidates themselves. Lamb is a vastly different D than the 2016 "D" in the sample. Is that heteroskedastic?

That's your subjective opinion.

Another subjective opinion I hold is that scientism is a big problem.

The claim though is a bit more general than this particular special election in PA. To 'debunk' it you would typically be expected to provide an alternative explanation for every one of those 6 swings. Arguably, these would become more and more complicated until your model includes several extra variables to explain each case. My model only has one variable, Trump. Doesn't mean the more complex model can't be true, but in scientific inference generally the simpler model is preferred.

Are you suggesting that your model reliably approximates the truth?

[Poopadoop]

I'm referring to the change in the D candidates themselves. Lamb is a vastly different D than the 2016 "D" in the sample. Is that heteroskedastic?

Your argument is that, in PA, something besides Trump being president changed between 2016 and 2017 that resulted in the D swing. That's got nothing to do with heteroscedascity. Even if it were possible for one predictor and one outcome varaible to result in heteroscedascity (it isn't), the binomial analysis doesn't assume homoscedascity, so it's not relevant. If it were an ANOVA then that assumption would have to be upheld.

My response to your argument is that explanation only works in PA, it can't explain the other five data points.

Are you suggesting that your model reliably approximates the truth?

I'm suggesting in the absence of a more compelling explanation it does pretty good, yes. For each election, it's obvious that different things are going to factor into what happens. We can't quantify what those things are or how they affect the swings. Certainly Conor's a different candidate than the last guy and that makes a difference. But it only makes a difference in PA, and we don't know how much of a difference it made. Further, all kinds of other things will have effects as well, because lots of things change between 2016 and 2017, not just the candidates in those special elections. Without any systematic reason to think those changes all favoured D candidates, then the appropriate assumption is that the effects of those unknown variables would tend to self-correct, i.e., cancel each other out.

The one common denominator in all these special elections is that they took place since Trump was elected. So that is one variable that arguably should impact all their outcomes. Further a change in the PA S.E should not affect the five special elections other states, and certainly not when those elections occurred BEFORE the one yesterday. So to exclude Trump as a cause, you need to go through each S.E. and make separate arguments for why each of those had the swing it had. Maybe you can do that convincingly, I don't know. But until someone does, I'm holding the simplest, one variable explanation as my model.