### Methodology on the Margins

I'm thinking again about experiments that we run. Yes, they are usually messy. In my last post, I talked about the inherent messiness of survey experiments that is due to the fact that surveys have many design features to consider. And these features may interact in ways that mean we can't simply pull out an experiment on a single feature and generalize the result to other surveys.

But I started thinking about other problems we have with experiments. I think another big issue is that methodological experiments are often run as "add-ons" to larger surveys. It's hard to obtain funding to run a survey just to do a methodological experiment. So, we add our experiments to existing surveys.

The problem is that this approach usually creates a limitation. The experiments can't risk creating a problem for the survey. In other words, they can't lead to reductions in response rates or threaten other targets that are associated with the main (i.e. non-methodological) objective of the survey. The result is that the experiments are often contained to things that can only have small effects.

A possible exception is when a large, ongoing survey undertakes a re-design. The problem is that this only happens for large surveys, and the research is still formed by the objectives of that particular survey. I'd like to see this happen more generally. It would be nice to have some surveys that have a methodological focus that could provide results that generalize to a population of smaller-scale surveys. Such a survey could also have a secondary substantive goal.

### "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…