### What is "responsive design"?

This is a question that I get asked quite frequently. Most of what I would want to say on the topic is in this paper I wrote with Mick Couper a couple of years ago.

I have been thinking that a little historical context might help in answering such a question. I'm not sure the paper we wrote does that. I imagine that surveys of old were designed ahead of time, carried out, and then evaluated after they were complete. Probably too simple, but it makes sense. In field surveys, it was hard to even know what was happening until it was all over.

As response rates declined, it became more difficult to manage surveys. The uncertainty grew. Surveys ended up making ad hoc changes more and more frequently. "Oh no, we aren't hitting our targets. Increase the incentive!" That seems like a bad process. There isn't any planning, so bad decisions and inefficiency are more likely. And it's hard to replicate a survey that includes a "panic" phase.

Not to put words in their mouths, but Groves and Heeringa wanted to address this situation. They give a conceptual outline for how to do so. Their approach emphasizes pre-planning (risk management) and experimentation aimed at making optimal or nearly-optimal choices with the information at hand.

To me, that's a dividing line between "ad hoc"changes and responsive design. That allows to create reproducible procedures. It also allows us to design surveys in a way that could be described as optimal, given information deficits (i.e. uncertainty).

We could probably look backwards to the "pre-Responsive Design" era and find examples of responsive design. But Groves and Heeringa gave us a systematic way to think about the problem and to create replicable research.

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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…

### 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…