### A Twist on Feedback

In my last post, I talked about thinking about data collected between attempts or waves as "feedback" from sampled units. I suggested that maybe the protocol could be tailored to this feedback.

Another to express this is to say that we want to increase everyone's probabilities of response by tailoring to their feedback. Of course, we might also make the problem more complex by "tailoring" the tailoring. That is, we may want to raise the response probabilities of some individuals more than that of other individuals. If so, might we consider a technique that is more likely to succeed in that subset. I'm thinking of this as a decision problem.

For example, assume we can increase response probabilities by 0.1 for all cases with tailoring. But we notice that two different techniques have this same effect.

1) The first technique increases everyone by 0.1.
2) The second  technique increases a particular subgroup (say half the population) by 0.15 and everyone else by 0.

We might prefer the latter if it reduces some other indicator for the risk of nonresponse bias more than the former. The response rate would definitely prefer the former.

Or, we might have two techniques, one has a big variance in the estimated impact for the subgroup and low variance overall and the other has low variance for the subgroup and high variance for everyone else. We might prefer the latter technique if something other than the response rate is our reward function.

### "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…