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Learning from paradata

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 in sequence or targeting modes is little understood. Here, the history of the case and other paradata might be helpful. We have an example of this in our new book on adaptive design.

The second area where I think a dynamic treatment regime approach might be useful is panel surveys. Panel surveys are more like the chronic illnesses for which the dynamic treatment regimes are used. In a panel survey, persons are interviewed multiple times and optimizing that might mean a long enough sequence for some learning to occur.

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