### Quasi-Experiments and Nonresponse

In my last post, I talked about using future data collection as a quasi-experimental validation of hypotheses about nonresponse. I thought I'd follow up on that a bit more.

We often have this controversy when discussing nonresponse bias: if I can adjust for some variable, then why do I need to bother making sure I get good response rates across the range of values that variable can take on? Just adjust for it.

That view relies on some assumptions. We assume that no matter what response rate I end up at, the same model applies. In other words, the missing data only depend on that variable at every response rate I could choose (Missing at Random). The missing data might depend only on that variable for some response rates but not others.

In most situations, we're going to make some assumptions about the missingness for adjustment purposes. We can't test those assumptions. So no one can ever prove you wrong.

I like the idea that we have a hypothesis at an interim point in the data collection. We might make this hypothesis very specific by predicting values for the missing cases. Then we add some addtional interviews and compare our predictions for those cases to the newly observed data. Does this confirm our hypothesis? Do we make new predictions for the remaining cases now that we have some additional data? In this setup, we can at least partially check our assumptions as we go.

### "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…