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Adaptive Design in Panel Surveys

I enjoyed Peter Lugtig's blog post on using adaptive design in panel surveys. I was thinking about this again today. One of the things that I thought would be interesting to look at would be to view the problem of panel surveys as maximizing information gathered.

I feel like we view panel studies as a series of cross-sectional studies where we want to maximize the response rate at each wave. This might create non-optimal designs. For instance, it might be more useful to have the first and the last waves measured, rather than the first and second waves. From an imputation perspective, in the latter situation (first and last waves) it is easier to impute the missing data.

The problem of maximizing information across waves is more complicated than maximizing response at each wave. The former is a sequential decisionmaking problem, like those studies by Susan Murphy as "adaptive treatment regimes." It might be the case, that a lower response rate in early waves might lead to overall higher information -- if it can lead to more data later. It's certainly a complicated problem, but one worth considering.

For example, would postponing refusal conversion across several waves increase the probability of responding to more waves?  A recent article by Burton and colleagues looked at the effect of refusal conversion on panel composition. People tended to stay in after being converted, but eventually dropped out. This is a useful evaluation of refusal conversion. It might also be useful to examine whether delaying refusal conversion increases the number of waves of response.

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