Virtually every story about election polling includes some treatment of cross-tabs. These articles typically cherry-pick sub-groups of voters whose preferences seem unusually striking. They can also be highly misleading, because they often rely on statistically insignificant samples and conflate the impact of collinear variables–for example, single women reliably vote Democratic, but that is largely because they tend to be younger than married women are. Our story \”All politics is identity politics\” avoided both of these pitfalls. It utilised a sufficiently large database (with 125,000 individual responses) to provide reliable estimates of the opinions of small sub-groups. And it used logistic regression to isolate the impact of each variable, with all others held constant. This yielded powerful new insights, such as the pattern that among blacks, younger voters are actually more conservative than older ones—perhaps because they have experienced less direct exposure to segregation. Our story \”British voters are unimpressed by Theresa May’s Brexit deal\” took a similar approach, although we wanted to give our readers a sense of what the country thought of three options (remain, May\’s deal and no deal), and where the fault lines in the electorate lay.
What makes this project innovative?
What was the impact of your project? How did you measure it?
Source and methodology
- https://www.economist.com/graphic-detail/2019/02/22/profiles-of-a-divided-country https://www.economist.com/graphic-detail/2019/02/23/british-voters-are-unimpressed-by-theresa-mays-brexit-deal