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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.

### "Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

### Future of Responsive and Adaptive Design

A special issue of the Journal of Official Statistics on responsive and adaptive design recently appeared. I was an associate editor for the issue and helped draft an editorial that raised issues for future research in this area. The last chapter of our book on Adaptive Survey Design also defines a set of questions that may be of issue.

I think one of the more important areas of research is to identify targeted design strategies. This differs from current procedures that often sequence the same protocol across all cases. For example, everyone gets web, then those who haven't responded to  web get mail. The targeted approach, on the other hand, would find a subgroup amenable to web and another amenable to mail.

This is a difficult task as most design features have been explored with respect to the entire population, but we know less about subgroups. Further, we often have very little information with which to define these groups. We may not even have basic household or person chara…

### An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea.

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample.

Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assum…