### Responsive Design is not just Two-Phase Sampling

I recently gave, along with Brady West, a short course on paradata and responsive design. We had a series of slides on what is "responsive design." I had a slide with a title similar to that of this post. I think it was "Responsive Design is not equal to Two-Phase Sampling."

I sometimes have a discussion with people about using "responsive design" on their survey, but I get the sense that what they really want to know about is two-phase sampling for nonresponse.

In fact, two-phase sampling, to be efficient, should have different cost structures across the phases. But the requirements for a responsive design are higher than that. Groves and Heeringa also argued that the phases should have 'complementary' design features. That is, each phase should be attractive to different kinds of sampled people. The hope is that nonresponse biases of prior phases are cancelled out by the biases of subsequent phases.

Further, responsive designs can exist without two-phase sampling. Design features can be complementary, have similar costs, and not necessarily be offered to subsets of nonrespondents from the prior phase.

Two-phase sampling for nonresponse was an important innovation. But I'd argue that Groves and Heeringa also came up with an important innovation which is actually different and new.

1. Regarding the first reason you give about why responsive design is not two-phase sampling: can't each phase in a two-phase (or multi-phase) sampling also be attractive to different kinds of sampled people? The way I see, two-phase sampling is just the method, but you can use different, complementary design features in each phase, so that you attract different types of respondents. In that sense to me, responsive design is not different from two-phase sampling, it's just that the former used the latter to implement it.

Can you give an example of a responsive design that does not use two-phase sampling, as you suggest in the forth paragraph?

2. With regard to the first reason, my point is that the sampling isn't a design feature. Something else has to change. The original Hansen and Hurwitz article changed the mode. I find that non-samplers tend to think of two-phase sampling as somehow magic. Almost like a way to cheat on the response rate. So, emphasizing the other design change forces them to think about the possibilities.

On your second question, if I had a design feature that didn't cost more, then I could at least consider switching the design without sampling. For example, if I want to offer a shortened survey to nonresponders, I don't necessarily have to subsample in order to do this. The sampling is usually related to costs... or rare resources. For instance, you might subsample in order to give the remaining sample to a subset of the "best" interviewers, however, that is defined.

### "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…