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Probability Sampling

In light of the recent kerfuffle over probability versus non-probability sampling, I've been thinking about some of the issues involved with this distinction. Here are some thoughts that I use to order the discussion in my own head:

1. The research method has to be matched to the research question. This includes cost versus quality considerations. Focus groups are useful methods that are not typically recruited using probability methods. Non-probability sampling can provide useful data. Sometimes non-probability samples are called for.

2. A role for methodologists in the process is to test and improve faulty methods. Methodologists have been looking at errors due to nonresponse for a while. We have a lot of research for using models to reduce nonresponse bias. As research moves into new arenas, methodologists have a role to play there. While we may (er... sort of) understand how to adjust for nonresponse, do we know how to adjust for an unknown probability of getting into an online panel? That's not my area, but certainly something worth looking at. Probably election polling is most developed in this area.

Related to this, how to we know when something is bad? Tough question, but methodologists ought to lead the way in developing methods to evaluate it.

3. At least some probability surveys are still needed. For example, in election polling, likely voter models are an important ingredient of estimates. Such models can be tested and developed on panel surveys like the National Election Study, and then applied to other samples. Mick Couper reviews uses of surveys in the age of "big data." Yes, we do still need them.
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