### Simulation of Limits

In my last post, I advocated against truncating effort. In this post, I'm going to talk about doing just that. Go figure.

We were discussing call limits on a project that I'm working on. This is a study that we plan to repeat in the future, so we're spending a fair amount of time experimenting with design features on this first wave.

There is a telephone component to the survey, so we've been working on the question of how to specify the calling algorithm and, in particular, what if any ceiling we should place on the number of calls.

One way to look at it is to look at the distribution of final outcomes by call number -- sort of like a life table. Early calls are generally more productive (i.e. produce a final outcome) than late calls. You can look at the life table and see after which call very few interviews are obtained. You might truncate the effort at that point.

The problem is that simulating what would happen if you place a ceiling on the number of calls isn't the same thing as actually placing a limit on calls. Especially in a phone lab. In the phone lab, if you don't do something (place a call on a case over the limit), then you are going to call a case that wouldn't have otherwise been called. Even if there are fewer hours of interviewing, this is likely to change how the calls are distributed over time.

Telephone labs are a complex system. Sometimes it feels like every time you turn a knob to change one setting, something unexpected happens somewhere else in the system. It makes for an interesting problem.

### "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…