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Hickman on the Gallup Daily

Topics: 2008 , Barack Obama , Gallup , Harrison Hickman , Hillary Clinton , John Edwards , Sampling

Yesterday, in a burst of blogger exuberance, I posted some charts emailed by my long ago employer Harrison Hickman, the Democratic pollster now associated with the firm Global Strategy Group who also conducted surveys earlier this year for John Edwards. I gave Hickman and his associate credit for the charts but then provided my own interpretation, comments I subsequently qualified. This morning, I did what I should have done in the first place, which is offer Hickman the opportunity to describe the charts in his own words. Harrison's summary follows below.
--Mark Blumenthal

The initial (and only) purpose of the line charts Ben Margolis and I sent Mark yesterday seems to have been obscured by our failure to provide explanation with the charts and some of the verbal vines their publication stimulated ("Day-of-Week Effect in Gallup Daily?"). Hopefully I can provide something of a corrective for the former.

1. We submitted the charts without explanation but with obvious doubts about their significance, statistical and otherwise. The subject line of my original e-mail to Mark was "spurious or what?"

2. The point of the exercise was to note that all the hoopla about Obama or Clinton being ahead or behind by more or less at a specific point was ignoring a persistent pattern in the data. (The time period covered was since the departure of the Sainted Senator Edwards.) The point was that the hoopla was misguided, not that the pattern itself is all-telling. We certainly never intended to suggest that particular changes could be associated with specific days or that there was any iron law of anything at work. Our message: if you don't like the results today, wait a couple of days. If you do, it might be wise to exercise some restraint. In that vein, Mark is correct in urging caution about reading too much into day-to-day changes. I would urge similar caution in the interpretation of two techniques under discussion here.

3. The rolling average technique was developed to introduce a cost-effective way to report opinion data more or less continually in critical points of a campaign, and there are a variety of different ways to calculate those averages. But it is important to note that the "smoothing" artifact is the reason the technique is useful, not the reason it is misleading. An on-going series of one-day polls would be more misleading for campaign professionals and poll consumers than rolling averages.

4. Perhaps the most important statistical point to understand about these types of polls is that a sample is not a sample until it is completed. Before its completion, a sample is not "random" even in the colloquial sense of the term. It is for this reason that no one should mistake the partial results of stand-alone samples as precise, no matter how extensively those partial data are weighted. This is particularly important to remember when confronted with early wave results of election day polls (exit polls).

5. One should be mindful of but not obsessed with any particular statistical test. Estimation error is the most reported but hardly the only type of error in opinion research. It is treated as more important than it is and than the other types of errors because (a) it has the veneer of precision because it is a number and (b) it easier to understand and better researched than other categories of errors. Here is a simple measure of the its importance: "sampling error" so-called is taught in the introductory course but other types of errors are saved for later in a student's learning. Here's another: If you read any questionnaire carefully and think seriously about the methods used to gather the data, you almost always will find sources for potentially greater "error" than estimation error in what is reported.

6. In fact, a legitimate argument can be made that estimation errors are not really an applicable statistic for most opinion polls we see. The underlying assumption of sampling error is that the sample in question is random, and random has a very precise statistical definition. For a host of reasons, the samples in most polls do not qualify as random in a strict sense and, in too many cases, even under the loosest standards. Harris or Gallup (forgive me for mot remembering which) used to report a table of mathematical estimation error ranges but also something called ranges generated "from observation." I do not recall that they ever explained the source of the observations but found the presentation refreshing as an implicit statement about the limitations of statistical error calculations.

7. Two final observations from reading comments. As consumers and practitioners, recognize that political arguments are still political arguments even when they are dressed up with statistical language. And, finally, do not assume that any pollster is part of a larger conspiracy against your preferred candidate until you have ruled out (a) incompetence and (b) the possibility that things are not as rosy as you want them to be.

Harrison Hickman
Global Strategy Group, LLC

P.S. Not to suggest that there is a day-(or period-)of-the-week effect in the Gallup data, but as of a few minutes ago, it seems that the stop-the-presses 10-point "lead" Obama enjoyed this weekend is now four points. An up-to-date version of our original charts is below, including a line based on calculation beginning the week after Super Tuesday.

 

Comments
Nick Panagakis:


Ifs and buts and maybes.

Popular vote totals at realclearpolitics.com now show Obama ahead with 14.3M to 14.1M for Clinton - a 0.6% lead. This is based in 38 states that have voted so far including FL and MI and estimates in four smaller caucus states that did not release results.

Since many of the remaining states are smaller in terms of turnout, does this mean that perhaps as much as 85% of current Gallup samples are reporting how they DID vote, not how they WOULD vote? This methodology is not clear.

Harrison or Jeff Jones, if samples include past votes and if that reporting by respondents is totally reliable, are daily changes mostly due to sample error?

Nick Panagakis

P.S. Mark. I met Harrison in 1978. Hint: IL Dem running for Gov.


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