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How much has the response rate shaped our methods?

In recent posts, I've been speculating about what it might mean to optimize survey data collections to something other than the response rate. We might also look at the "inverse" problem -- how has the response rate shaped what we currently do? Of course, the response rate does not dominate every decisions that gets made on every survey. But it has had a far-reaching impact on practice. Why else would we need to expend so much energy reminding ourselves that it isn't the whole story?

The outlines of that impact are probably difficult to determine. For example, interviewers are often judged by their response rates (or possibly conditional response rates). If they were to be judged by some other criterion, how would their behavior change? For example, if interviewers were judged by how balanced their set of respondents were, how would that impact their moment-to-moment decision-making? What would their supervisors do differently? What information would sample management systems deliver to interviewers? What would project managers look at on a day-to-day basis?

It seems to me that it is difficult to see where the influence of the response rate begins and ends. At the moment, we are taking baby steps away from judging everything in terms of the response rate. We check sample balance, while seeking to maximize response rates. If the sample balance begins to be out of whack, we intervene to control the process a bit. But this is still a long way from what it might look like to be maximizing some function other than the response rate.
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