I just got back from JSM where I saw some presentations on responsive/adaptive design. The discussant did a great job summarizing the issues. He raised one of the key questions that always seems to come up for these kinds of designs: If you have those data available for all the cases, why bother changing the data collection when you can just use nonresponse adjustments to account for differences along those dimensions?

This is a big question for these methods. I think there are at least two responses (let me know if you have others).

First, in order for those nonresponse adjustments to be effective, and assuming that we will use weighting cell adjustments (the idea extends easily to propensity modeling), the respondents within any cell need to be equivalent to a random sample of the cell. That is, the respondents and nonrespondents need to have the same mean for the survey variable. A question might be, at what point does that assumption become true? Of course, we don't know. But if we alter our data collection strategy, we will at least strive to empirically verify that over some range of response rates.

Second, this is an empirical question. It would be nice to have studies that looked at this question. Does balancing response along specified dimensions lead to reduced nonresponse bias after adjustment? My hunch is "yes, it does."

1. Hi James,
Not an expert on this, but wouldn't you say that with nonresponse adjustments, you expect the transformation to be linear (or at least parametric), whereas if you adjust the data collection, adjustments can take any form. I agree with you that this may or may not matter in practice, and that that's an empirical question.

2. Peter, Thanks for the interesting comment. It seems to me that when we make our nonresponse adjustments, after all is said and done, we postulate that our model is correct and that the adjusted measures are unbiased (or as unbiased as can be given the available data). Normally, this assumption can't be tested. If there is an unobserved covariate that is making that assumption invalid, we can't know it.

When we are collecting data, it is as if we assume that we are breaking (or maybe reducing) the correlation between that covariate and response, conditional on all the observed data.

My hunch is that trying to break the correlation during data collection is worthwhile. Sadly, in most cases, we may never know. Today we lean pretty heavily on the Pew studies first reported in 2000 as evidence that response rate may not be a good indicator. It would be nice to have similar empirical studies on this question.

3. I entirely agree, both about reducing the potential for bias during data collection and about the unfortunate lack of evidence.

To me, the argument for reducing rather than only adjusting using the same available information is one about robustness of the design with respect to nonresponse bias. Relying only on adjustments leaves greater potential for nonresponse bias due to heterogeniety within adjustment cells/groups. Doing something to equalize response rates across cells should reduce this risk. I think this is the same as what you said, stated in a slightly different way.

One of the challenges with the empirical evidence is that there may not be substantial bias in weighted estimates to begin with, in which case one would erroneously conclude that only adjusting is fine. Yet it is about robustness - reducing the risk of bias, when there is bias...

1. I think you are right that it is more robust to control the data collection in this way.

I also think it may be the case that we can reduce the variability induced by interviewers by giving more centralized direction. Just conjecture...

### "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…