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Balancing Response

I have been back from the AAPOR conference for a few days. I saw several presentations that had me thinking about the question of balancing response. By "balancing response," I mean actively trying to equalize response rates across subgroups. I can define the subgroups using data that are complete (i.e. on the sampling frame or paradata available for responders and nonresponders).

I think there probably are situations where balancing response might be a bad thing. For instance, if I'm trying to balance response across two groups, persons 18-44 and 45+, and I have a 20% response rate among 18-44 year olds and a 70% response rate among 45+ persons, I might "balance response" by stopping data collection for 45+ persons when I get a 20% data collection. It's always easy to lower response rates. It might even be less expensive to do so.

But I think such a strategy avoids the basic problem. How might I optimize the data collection to reduce the risk of nonresponse bias? In my mind, that implies allocating your resources differentially. In the example I just gave, I think that would mean reallocating resources from older persons to younger persons. I saw an interesting presentation from the Census Bureau on the National Survey of College Graduates that did something like that.

Of course, this opens up new questions.... like how do we account for sampling error in this allocation? And, why not just adjust for the different response rates after the survey is complete? I'll come back to that later.

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