### The Dual Criteria for a Useful Survey Design Feature

I've been working on a review of patterns of nonresponse to a large survey on which I worked. In my original plan, I looked at things that are related response, and then I looked at things that are related to key statistics produced by the survey. "Things" include design features (e.g. number of calls, refusal conversions, etc.) and paradata or sampling frame data (e.g. Census Region, interviewer observations about the sampled unit, etc.).

We found that there were some things that heavily influenced response (e.g. calls) that did not influence the key statistics. Good, since more or less of that feature, although important for sampling error, doesn't seem important with respect to nonresponse bias.

There were also some that influenced the key statistics but not response. For example, interviewer observations we have for the study. The response rates are close across subgroups of these estimates. As a result, I won't have to rely on large weights to get to unbiased estimates. Or, in another way of looking at it, I empirically tested what estimates would have looked like had I relied on that assumption at an earlier phase of the survey process.

And of course, there were some that predicted neither. And there were none that strongly predicted both response and key statistics.

This result seems good to me. Why? We haven't allowed any variables to be highly predictive of response. If we had, we would need to rely upon strong assumptions (i.e. large nonresponse adjustments) to motivate unbiased estimates. But we can also predict some of the key statistics. This relationship might be confounded, but it still seems good that we have some useful predictors of the key statistics.

In any event, organizing the analysis along these lines was helpful for me. I didn't develop a single-number characterization of the quality of our data, but I did tell a somewhat coherent story that I believe provides convincing evidence that our process produces good quality data.

### "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…

### Goodhart's Law

I enjoy listening to the data skeptic podcast. It's a data science view of statistics, machine learning, etc. They recently discussed Goodhart's Law on the podcast. Goodhart's was an economist. The law that bears his name says that "when a measure becomes a target, then it ceases to be a good measure." People try and find a way to "game" the situation. They maximize the indicator but produce poor quality on other dimensions as a consequence. The classic example is a rat reduction program implemented by a government. They want to motivate the population to destroy rats, so they offer a fee for each rat that is killed. Rather than turn in the rat's body, they just ask for the tail. As a result, some persons decide to breed rats and cut off their tails. The end result... more rats.

I have some mixed feelings about this issue. There are many optimization procedures that require some single measure which can be either maximized or minimized. I think thes…