Friday, September 30, 2011

Adaptive and Responsive Design

I've raised this topic a couple of times here. Several years ago, Groves and Heeringa (2006) proposed an approach to survey data collection that they called "Responsive Design." The design was rolled out in phases with information from prior phases being used to tailor the design in later phases.

In my dissertation, I wrote about "Adaptive Survey Design." For me, the main point of using the term "adaptive" was to link to the research on adaptive treatment regimes, especially as proposed by Susan Murphy and her colleagues.

I hadn't thought much about the relationship between the two. At the time, I saw what I was doing as a subset of responsive designs.

Since then, Barry Schouten and Melania Calinescu at Statistics Netherlands have defined "adaptive static" and "adaptive dynamic" designs. Adaptive static designs tailor the protocol to information on the sampling frame. For example, determining the mode of contact for each case by its characteristics on the frame, like age. Adaptive dynamic designs tailor the design to incoming paradata. A refusal conversion protocol might be a commonly used example. Changing incentives based on paradata might be another example. The "adaptive dynamic" designs seem to come closest to the kind of designs I envisioned when writing my dissertation.

Over the summer, Mick Couper and I gave a talk on responsive designs. We included some definitional discussion. It was Mick's idea to describe these designs along a continuum. The dimension of the continuum involves how much tailoring there is. On one end, single protocol surveys apply the same protocol to every case. On the other end of the spectrum, adaptive treatment regimes provide individually-tailored protocols. Here's a graphic:


The definitions of these various terms may still be fluid. The important thing is that folks who are working on similar things be able to communicate and build upon each others results.

Monday, September 26, 2011

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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