To my mind, a key reason for responsive designs is uncertainty. This uncertainty can probably occur in at least two ways. First, at a survey level, I can be uncertain about what response rate a certain protocol can elicit. If I don't obtain the expected response rate after applying the initial protocol, then I can change the protocol and try a different one.
Second, I can be uncertain about which protocol to apply at the case level. But I know what the protocol will be after I have observed a few initial trials of some starting protocol. For example, I might call a case three times on the telephone with no contact before I conclude that I should attempt the case face-to-face.
In either situation, I'm not certain about which protocol specific cases will get. But I do have a pre-specified plan that will guide my decisions during data collection. There is a difference, though, in that in the latter situation (case level), I can predict that a proportion of cases will receive the second, changed protocol. And in order to optimize, I need to be able to do that. Calinescu et al. make this observation.
In the survey level case, I don't necessarily need to do that, I can optimize in phases. Of course, everyone has to be willing to live with the results -- even if this means obtaining fewer than expected interviews. In some sense, my uncertainty extends to my final product which may differ from that I initially expected.
Second, I can be uncertain about which protocol to apply at the case level. But I know what the protocol will be after I have observed a few initial trials of some starting protocol. For example, I might call a case three times on the telephone with no contact before I conclude that I should attempt the case face-to-face.
In either situation, I'm not certain about which protocol specific cases will get. But I do have a pre-specified plan that will guide my decisions during data collection. There is a difference, though, in that in the latter situation (case level), I can predict that a proportion of cases will receive the second, changed protocol. And in order to optimize, I need to be able to do that. Calinescu et al. make this observation.
In the survey level case, I don't necessarily need to do that, I can optimize in phases. Of course, everyone has to be willing to live with the results -- even if this means obtaining fewer than expected interviews. In some sense, my uncertainty extends to my final product which may differ from that I initially expected.
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