“Supposing is Good, But Finding Out is Better”: The Value of Observational Data In Election Research


[Image courtesy of Mark Twain House and Museum]

On Tuesday, the Madison city clerk’s office held a mock election to test some of the effects of Wisconsin’s new photo ID law. As reported in the Capital Times, the results found that depending on the number of poll workers and the organization of the polling place, voters could expect to wait between one and four minutes per person in line.

This isn’t the only mock election Madison will conduct, either; Tuesday’s test didn’t include Election Day voter registration, which officials will observe and test with help from students from nearby UW-Madison.

The Madison study is a perfect embodiment of the Mark Twain quote that serves as this post’s title. While common sense suggests that adding steps to the voter check-in process will add time to the wait, the clerk’s office went ahead and tried to find out how much; moreover, the process allowed for some experimentation (adding pollworkers, splitting the pollbook, checking IDs at the door, etc.) that will help guide how polling places are staffed when real voters come through the door.

Even better, such research allows poll workers to become familiar with the new processes and moves “on the job” learning to a safer environment. The state Government Accountability Board has several hundreds of thousands of dollars to spend on programs to educate voters and pollworkers alike about the new law so sessions like this could become more frequent.

Observational data – where researchers generate data simply by observing a process – has been somewhat underappreciated in the elections world even as it becomes more prevalent in the private sector, with mystery shoppers and other observers testing a merchant’s customer service processes and providing guidance on how servce could improve. Such data can be a complement to other data like surveys, election statistics and the like as election offices seek to improve the voter experience.

Of course, the need for rigor and good design still applies. Over at Election Updates, MIT’s Charles observes that line length is driven not just by what’s happening at the front of the line but also who’s arriving at the back:

The effect of heavy foot traffic tends to grow the length of queues in a highly non-linear fashion. Think about rush hour, where you can go from smooth traffic flow to gridlock with the addition of just a few more cars.

There is also the so-called Hawthorne Effect, in which people tend to modify their behavior when they know they’re being observed. Such effects can lead to misleading results that might not reoccur when researchers aren’t present.

On balance, however, Madison’s mock election represents a promising and valuable example of using observation to improve the voting process. Hopefully other jurisdictions will find a way to follow Madison’s lead.

As another great American humorist – Yogi Berra – once said, “you can observe a lot just by watching.”

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