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Measurement Error in Paradata

Paradata are often quite messy. I guess it shouldn't be that surprising since they are often the by-product of a process (survey interviewing) that can be messy. And, at least initially, they were a means to an end and not the end itself.

But there are some issues that run a little deeper than just messy. Brady West had a very interesting paper at AAPOR that looked at measurement error in interviewer observations. On a large face-to-face survey, we ask interviewers to make guesses about key characteristics of selected persons. These guesses are (relatively) highly correlated with survey outcome variables. This is a useful property for many reasons -- monitoring for the risk of bias, adjustment, etc. But, as Brady points out, the measurement or misclassification error reduces their effectiveness.

I've been thinking about another kind of error. In talking with interviewers on the same face-to-face survey, they say the visit ever sampled housing unit every time they visit a segment (i.e. sampled neigbhorhood in a geographically clustered sample). This doesn't show up in the call records. I'm thinking that they may be driving through the segment and then selecting cases to call based on this initial pass through. Possibly based on clues they say -- car in driveway, people outside, etc. If so, the call records would have a kind of selection bias built in. And, it would be likely that interviewers were highly variable in their ability to detect these clues.
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