Using Statistical Inference in the Practice of Politics
Most people interested in politics have at least some exposure to the use of statistical inference at a very basic level--at the very least, most would accept that it does work and know of some specialists more adept than themselves at using it. Polls are stock-in-trade of most political news coverage and so every entry-level political junkie quickly develops some facility with the jargon necessary for trading suppositions based on horse-race polling.
Of course, a little experience lends the ever-so-slightly-seasoned political operative to learn that the real value of polling lays not in measuring where we are but in guiding resource allocation. The most important inferences (educated guesses about facts which cannot be directly observed) for campaigns to make have to do with the differential effect that issue messages or various framings of contestable issues have on persuadable voters—and the key information here is which messages move just enough voters at the lowest cost. In a campaign with scarce resources (and I've never seen any other kind) investing in information that helps you spend your communications budget wisely is almost always a good choice.
Micro-targeting is a deeper use of statistical inference that is becoming increasingly available to campaigns. It started with the largest (nationwide, presidential) campaigns, but the method has become more accessible to smaller (statewide, nationally-targeted congressional, and statewide-targeted legislative) campaigns. The practice is revolutionary for campaigns and work like this:
Start with a voter file. . .improve that voter file with commercially available data about consumer behavior (especially consumer behavior reasonably linked to political interests). . .draw a large sample form the improved voter file. . .poll large sample on a battery of political interest questions. . .regress poll results on sample to derive a model that relates political interests to consumer behavior. . .apply that model to the broader voter file to predict electoral behavior. . .use predictions to constrain persuasion and mobilization communication expenditures and reduce wasted communication.
Now, the casual observer might notice that there are quite a few steps of inference and supposition in that chain of reasoning, but these are sensible when performed by someone who fully understands the statistical pitfalls.
A deeper way to apply statistical inference to improve targeting and resource allocation is to combine micro-targeting predictions (and/or traditional polling results) with tactical field campaign results to further pare down target universes or to highlight mobilization segments.
Micro-targeting is revolutionary as a first application of bringing large datasets to bear to drive inferences about electoral behavior. But the state-of-the art has already begun to pass this technique by. The obvious innovation of combining micro-targeting predictions with tactical field data suggests a more powerful methodological move we could make. Field campaign data represents the tip of the iceberg when it comes to measurable campaign activity. Generally, field efforts in well-organized campaigns collect two dimensions of data (turnout intent and candidate preference) coded on a five- to seven-point scale. The development of statewide shared voter files through browser-based data applications has fostered the spread of best-practices, improved coding, and made it possible to collect, store, and accumulate such data across election cycles.
But vote choice and vote intent (while highly instrumental variables) are only a very narrow view into political behavior. Given that we have a wide array of tools available to deliver calls-to-action across a number of media and a number of corresponding tools to measure response we actually have the capacity to generate call and response filters that can define multi-dimensional segmentation for political audiences. This gives us an opportunity—if we will take it—to walk non-voters through a succession of calls-to-action and measured iterative communication to determine which paths will lead which sub-segments to convert to voters, to become our voters, or to adopt a shared identity.
If you are interested to learn how to implement these techniques in your campaign, we'd love to hear from you.