### Formalizing the Optimization Problem

I heard Andy Peytchev speak about responsive design recently. He raised some really good points. One of these was a "total survey error" kind of observation. He pointed out that different surveys have different objectives and that these may be ranked differently.  One survey may prioritize sampling error while another has nonresponse bias as its biggest priority. As there are always tradeoffs between error sources, the priorities indicate which way those decisions were or will be made.

Since responsive design has largely been thought of as a remedy for nonresponse bias, this idea seems novel. Of course, it is worth recalling that Groves and Heeringa did originally propose the idea in a total survey error perspective. On the other hand, many of their examples were related to nonresponse.

I think it is important to 1) think about these tradeoffs in errors and costs, 2) explicitly state what they are for any given survey,  and 3) formalize the tradeoffs. I'm not sure that we even usually get to step one, let alone to steps two and three.

By our training, survey methodologists ought to be able to do some of steps one and two. Operations Research and other fields such as Computer Science might be helpful for getting step three accomplished. Melania Calinescu, in her dissertation, used formal optimization methods to deal with explicitly stated objectives with respect to nonresponse and measurement errors. In some examples, she used existing data to identify designs that maximized the R-Indicator while constraining expected measurement error to certain limits.

These are tough problems to formalize, but doing so would, I think, be a real contribution to actually implementing the total survey error perspective... for responsive or any other kind of survey designs.

### "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…