### Reasons for maintaining high response rates

A few years ago, I was presenting at a conference of substantive experts. I gave an update on a progress on a survey of interest to this group. I talked about how nonresponse bias can be complex, and that the response rate might not be a good predictor of when this bias occurs -- based on Groves and Peytcheva. I was speaking with one of the researchers after my presentation, and I was surprised to hear her say that she interpreted my comments to mean that "response rates don't matter." Although that interpretation makes sense, it hadn't really occurred to me in that way until she said it.

Since then, it seems like we've seen a lot of published papers and conference presentation where lowering the response rate becomes a tactic for improving the survey. Most studies taking this tactic lower the response rates for groups that tend to respond at higher rates. The purported benefit is  response set balance on known characteristics from the sampling frame is improved. Some other studies show that lower response rates don't change estimates. A side benefit is that costs go down. One paper that I know of does have some validation data to show that nonresponse-adjusted estimates actually would have reduced bias with a strategy that lowered overall response rates in order to have more balanced response rates across subgroups.

There is some logic to this approach, But, I worry that lowering response rates might be a long-run bad policy. First, there is a minimum response rate, so this strategy can only work for so long. Second, once we lower response rates, we lose the ability to track what higher response rates might be getting. If a study has validation data, this might not be so bad, but for other studies, it means we won't have the necessary data for considering cost and error tradeoffs. Third, and this is a problem for the field that no particular study may want to address, but once we lower response rates, we lose the ability to obtain higher response rates. We won't know how to do it. So, despite the lack of a clear relationship with bias, there may still be reasons to maintain or (for the audacious) even increase response rates.

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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…

### 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…

### 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…