Mode Switching Algorithms

After running an experiment using sequences of modes (for contacting sampled households), I've been thinking about how to decide when to switch modes. In our experiment, we had a specified time when the switch would occur (after 5 weeks of the first mode, switch to the second mode).

It seems like better "switching" rules should be possible. Ideally, we would want to identify some best mode as quickly as possible. The amount of time it might take to determine this would vary across sampled cases.

The hard part is that we generally have very little feedback. We don't get a lot of information back from failed attempts. For example, a letter doesn't generally generate much feedback other than an interview occurred, it didn't occur, or the letter was returned. It might be that interviewer-administered modes are more promising for this kind of tailoring, since they do generally obtain more feedback.

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Assessment of Maching Learning Classifiers

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