### Survey Modes and Recruitment

I've been struggling with the concept of "mode preference." It's a term we use to describe the idea that respondents might have preferences for a mode and that if we can identify or predict those preferences, then we can design a better survey (i.e. by giving people their preferred mode).

In practice, I worry that people don't actually prefer modes. If you ask people what mode they might prefer, they usually say the mode in which the question is asked. In other settings, the response to that sort of question is only weakly predictive of actual behavior.

I'm not sure the distinction between stated and revealed preferences is going to advance the discussion much either. The problem is that the language builds in an assumption that people actually have a preference. Most people don't think about survey modes. Most don't consider modes abstractly in the way methodologists might. In fact, these choices are likely probabilistic functions that hinge on the characteristics of survey (contact mode, etc.) and unobserved characteristics of the sampled person (e.g. are they busy when they get the request). Is it a preference if one day I like and the next I don't? That might be a bit of hyperbole, but I don't believe that most people actually have stable mode preferences.

For me, the interesting thing is to identify the probability of response under different modes for subgroups in the population. That way, we can trade off errors and costs in order optimize surveys. Further, mixed modes might be a natural fit if we acknowledge that unobserved characteristics are influencing the decision to participate. I might do a web survey this month, but next month it would be easier to catch me by phone. My schedule changes in ways that the survey organization can't observe.

An interesting question might be, what characteristics can we observe or in a panel survey ask about in wave 1, that help us predict participation rates under different modes? I'm not sure what those might be, but interesting to explore.

### "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…