### Defining phases

I have been working on a presentation on two-phase sampling. I went back to an old example from an RDD CATI survey we did several years ago. In that survey, we defined phase 1 using effort level. The first 8 calls were phase 1. A subsample of cases was selected to receive 9+ calls.

It was nice in that it was easy to define the phase boundary. And that meant that it was easy to program. But, the efficiency of the phased approach relied upon their being differences in costs across the phases. Which, in this case, means that we assume that cases in phase two require similar levels of effort to be completed. This is like assuming a propensity model with calls as the only predictor.

Of course, we usually have more data than that. We probably could create more homogeneity in phase 2 by using additional information to estimate response probabilities. I saw Andy Peytchev give a presentation where they implemented this idea. Even just the paradata would help. As an example, consider two cases:

1. We've called this case 8 times. No one has ever answered. No answering machine.
2. We've called this case 8 times. We spoke to a person 3 times and schedule an appointment that was subsequently missed.

I'd bet our chances our better with the second case.

If we did build a propensity model and used the results, then we would just need to be careful that the estimates near the boundary are consistent across the field period (see my previous post on this topic).

### "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…