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Mixed-Mode Surveys: Nonresponse and Measurement Errors

I've been away from the blog for a while, but I'm back. One of the things that I did during my hiatus from the blog was to read papers on mixed-mode surveys. In most of these surveys, there are nonresponse biases and measurement biases that vary across the modes. These errors are almost always confounded. An important exception is Olson's paper. In that paper, she had gold standard data that allowed her to look at both error sources. Absent those gold standard data, there are limits on what can be done.

I read a number of interesting papers, but my main conclusion was that we need to make some assumptions in order to motivate any analysis. For example, one approach is to build nonresponse adjustments for each of the modes, and then argue that any differences remaining are measurement biases. Without such an assumption, not much can be said about either error source. Experimental designs certainly strengthen these assumptions, but do not completely unconfound the sources of error.

Having said that, gold standard studies, like Olson's, are an important step to test the validity of these kinds of assumptions. It seems that more such studies, focused on disentangling at least two error sources, would be very useful.




Comments

  1. I can recommend the work of my colleague Jorre Vannieuwenhuyze. He laid out several methods to disentangle such errors. http://scholar.google.nl/citations?user=ekOuDiwAAAAJ&hl=nl

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  2. Thanks! I'm familiar with his work. My point is that we need some assumptions to motivate any such method. There isn't any magic available!

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