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Mr. P by another name . . . is still great!

Brendan Nyhan points me to this from Don Taylor:

Can national data be used to estimate state-level results? . . . A challenge is the fact that the sample size in many states is very small . . . Richard [Gonzales] used a regression approach to extrapolate this information to provide a state-level support for health reform:

To get around the challenge presented by small sample sizes, the model presented here combines the benefits of incorporating auxiliary demographic information about the states with the hierarchical modeling approach commonly used in small area estimation. The model is designed to “shrink” estimates toward the average level of support in the region when there are few observations available, while simultaneously adjusting for the demographics and political ideology in the state. This approach therefore takes fuller advantage of all information available in the data to estimate state-level public opinion.

This is a great idea, and it is already being used all over the place in political science. For example, here. Or here. Or here.

See here for an overview article, “How should we estimate public opinion in the states?” by Jeff Lax and Justin Phillips.

It’s good to see practical ideas being developed independently in different fields. I know that methods developed by public health researchers have been useful in political science, and I hope that in turn they can take advantage of the progress we’ve made in multilevel regression and poststratification.