### Mechanisms of Mode Choice

Following up yet again, on posts about how people choose modes. In particular, it does seem that different subgroups are likely to respond to different modes at different rates. Of course, with the caveat that it's obviously not just the mode, but also how you get there that matters.

We do have some evidence about subgroups that are likely to choose a mode. Haan, Ongena, and Aarts examine an experiment where respondents to a survey are given a choice of modes. They found that full-time workers and young adults were more likely to choose web over face-to-face.

The situation is an experimental one that might not be very similar to many surveys: Face-to-face and telephone recruitment to the choice of face-to-face or web survey. But at least the design allows them to look at who might make different choices.

It would be good to have more data on persons making the choice in order to better understand the choice. For example, information about how much they use the internet might be useful. Is that the confounding variable that makes it seem like younger people prefer web surveys over CAPI? It would also be good to observe these choices in other settings.

A key to making progress on understanding the impact of mode (and how you get to the mode) is understanding the mechanisms. I'm not sure that we have done enough to understand that. Another opportunity for research...

### "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…