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When should we use the term "nonresponse bias"?

Maybe I'm just being cranky, but I'm starting to think we need to be more careful about when we use the term "nonresponse bias." It's a simple term, right? What could be wrong here?

The situation that I'm thinking about is when we are comparing responders and nonresponders on characteristics that are known for everyone. This is a common technique. It's a good idea. Everyone should do this to evaluate the quality of the data.

My issue is when we start to describe the differences between responders and nonresponders on these characteristics as "nonresponse bias." These differences are really proxies for nonresponse bias. We know the value for every case, so there isn't any nonresponse bias.

The danger, as I see it, is that naive readers could miss that distinction. And I think it is an important distinction. If I say "I have found a method that reduces nonresponse bias," what will some folks hear? I think such a statement is probably too strong when I'm talking about differences between responders and nonresponders on known characteristics.

On the other hand, I was talking with some folks about this a couple of weeks ago. No one agreed with me on this point.
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  1. RaphaelMay 1, 2014 at 1:40 AM

    I agree with you, especially when it comes to the idea that some laymen (or naive readers, as you put it) might have when something like that is said. It does sound like something that probably it isn't. I, myself, have the same problem with the usage of the term "representative" to describe a sample. But maybe I'm being too puristic...

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