For the beginning of August, MSN has been running daily polls on its election 2016 page and I have been helping. We ask between 8 and 9 questions per day. Generally, that includes 4-5 issues of the day: some of them regularly occurring issues like support for gun control or immigration, and others germane to the day like concern over tax returns or interest in the upcoming debate. Then we ask a series of demographic questions: age, gender, party identification. And, one more key question, the vote intention of the respondent.
While people take the poll, they see the raw results, but on this page we are going to highlight the analyzed results. Every poll you every see has raw results and analyzed results. The New York Times provided raw polling results, from Florida, to four respected pollsters (including me!) and showed how they got four polished, analyzed poll results from them ranging from Trump +1 to Clinton +4. The raw poll numbers: Clinton +8. There is a lot of interpretation in the analysis, but we believe that our procedure is the best for this type of opt-in polling data.
What we do is pretty advanced, but also very intuitive: we model and post-stratify the data. We run the multilevel regression with post-stratification (MRP), following the general principles described in this recent academic paper. Seriously, let me slow that down …
First, we model the raw response data of vote intention, given the following respondent characteristics: age, gender, state, and party identification. This information divides the population into hundreds of categories of demographics and we predict the percent of people in each category that would poll for Clinton, Trump, or Other, if the entire country showed up to the poll. The feature here is that every one of those predictions is informed by all polling responses. This is really important, because some/most of the demographic combinations do not come to the poll on any given day.
Second, we then projected these predictions on our best-estimate of the likely voting population. From various information we can estimate the number of voters in each of these categories who will turn out to vote in November.
What we get is not perfect; it reflects the very non-random non-representative sample of respondents coming to the poll. But, it does provide us a glimpse into what MSN readers, and the general voting population, are thinking about in regard to the upcoming election. Consider this experimental research, with an amazing data-set. And, I look forward to keeping you updated as it progresses over the next few weeks.