### 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 assumed to have its own intercept which is from a $N(0,\sigma^{2}_{il})$ distribution. The model is estimated:

$Pr ( R_{ijl} = 1 ) = logit^{-1} ( \beta_{0l} + \beta_{0il} + \sum_{j=1}^{k} \beta_{jl} X_{ijl})$

The next step is to compare the estimated contact probabilities within a household and find the window with the highest probability of contact for that household. In that window, the case -- along with all other cases that meet this criterion -- will be sorted to the top of list by the call scheduling algorithm. Under this approach, a case with a low probability of contact could be sorted to the top of the list in any given call window, as long as the estimated probability of contact was highest for the case within that window.

The experimental design required frequent sorting of the list as the call windows were specific to the time zone. For example, on a Tuesday, the list was sorted first thing in the morning, at 5pm EST, 6pm EST, 7pm EST, and at 8pm EST as the various time zones crossed the call window boundary. The experimental design required that the experimental and control groups be sorted in an intervleaving fashion. The past practice had been to sort at the beginning of the day. The sort was based on sorting more promising cases to the top -- cases with more contacts, selected respondents, number of calls and so on.

So far, the experiment has been going well. In the first month, the contact rate for the experimental group had a 15% increase relative to the control group.

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### "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…