Survey Methods Musings

Susan Murphy's work on dynamic treatment regimes had a big impact on me as I was working on my dissertation. I was very excited about the prospect of learning from the paradata. I did a lot of work on trying to identify the best next step based on analysis of the history of a case. Two examples were 1) choosing the lag before the next call and the incentive, and 2) the timing of the next call. At this point, I'm a little less sure of the utility of the approach for those settings. In those settings, where I was looking at call record paradata, I think the paradata are not at all correlated with most survey outcomes. So it's difficult to identify strategies that will do anything but improve efficiency. That is, changes in strategies based on analysis of call records aren't very likely to change estimates. Still, I think there are some areas where the dynamic treatment regime approach can be useful. The first is mode switching. Modes are powerful, and offering them i

I recently finished teaching a short course on responsive survey design . We had some interesting discussions. One of the things that we emphasized was the pre-planned nature of responsive design. We contrast responsive design with ad hoc changes that a survey might make in response to unanticipated problems. The reasoning is that ad hoc changes are often done under pressure and, therefore, are likely to be less than optimal -- that is, they might be implemented too late, cost too much, or better options might not be considered. Further, it's hard to replicate the results when decisions are made this way. Some of the students seemed uneasy about this definition. In part, I think this was because there was a sort of implication that one shouldn't make ad hoc changes. That really wasn't our message. Our point was that to be responsive design, it needs to be pre-planned. We didn't mean that if unanticipated problems arise, it would be better to do nothing. In this sense,