Monday, November 2, 2020

Rethinking Likely Voter Models

At this point just throwing out a lot of post as quickly as I can and relying too much on my phone’s not so reliable dictation app. Apologies in advance.

1. Most journalists are horribly confused by the differences between gathering and aggregating polling data and building likely voter models. Two completely different processes using entirely different tools prone to entirely different types of failure.

2. Of the two parts of the process, the modeling component will tend to be less stable and more vulnerable to the kind of changing conditions that can undercut basic assumptions.

3. It is impossible to believe that there is not a strong relationship between likely voter models and voter suppression. This is something we need to think more about and discuss more openly.

4. One of the implications of 3. is that changes in the effectiveness of voter suppression techniques can completely upend likely voter models. Remember the J shaped curve. There is considerable evidence showing that in many states the effect of voter suppression has reversed directions in a big way.

5. Likely voter models were also based on historical data from a period where early voting was much less accessible and played a much smaller role.

6. The models were also based on (at the risk of being obvious) non-pandemic data. This also has the potential to greatly undermine their predictive power this year.

7. Finally, returning to the subject of early voting. I wonder if the modelers working on these problems have been overly static in their thinking. If a month in advance of the election, you see a large number of people whom you have pegged as unlikely to vote having already voted, shouldn’t there be a way of using this information to adjust your models?

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