In the same vein as previous posts, I'm continuing to think about current practices that might be recast as adaptive.

Call limits are a fairly common practice. But they are also, at least for projects that I have worked on, notoriously difficult to implement. For example, it may happen that when project targets for numbers of interviews are not being met, then these limits will be violated.

We might even argue that since the timing of the calls is not always well regulated, that it is difficult to claim that cases have received equal treatments prior to reaching the limit. For example, three calls during the same hour is not likely to be as effective as three calls placed on different days and times of day. Yet they would both reach a three-call limit. [As an aside, it might make more sense to place a lower-limit on "next call" propensities estimated from models that include information about the timings of the call, as Kreuter and Kohler do here.]

In any event, subject to some modification, call limits do imply an adaptive rule where there are two possible design protocols: 1) make another call, 2) stop calling. The rule might take the following form: after the third call, stop if there has never been contact. The tailoring variable is the contact history (ever contact?) and the number of calls. Both of these are drawn from paradata.

In my view, these sorts of stopping rules are adaptive.

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