Do you really believe that?

I had an interesting discussion with someone at a conference recently. We had given a presentation that included some discussion of how response rates are not good predictors of when nonresponse bias might occur. We showed a slide from Groves and Peytcheva.

Afterwards, I was speaking with someone who was not a survey methodologist. She asked me if I really believed that response rates didn't matter. I was a little taken aback. But as we talked some more, it became clear that she was thinking that we were trying to argue for achieving low response rates. I thought it was interesting that the argument could be perceived that way.

To my mind, the argument wasn't about whether we should be trying to lower response rates. It was more about what tools we should be using to diagnose the problem. In the past, the response rate was used as a summary statistic for discussing nonresponse. But the evidence from Groves and Peytcheva calls into question the utility of that single statistic. My conclusion from that is that we need to work harder to really diagnose the risks of nonresponse bias. We need to view a constellation of statistics, developed under a variety of assumptions.

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