### Do response propensities change with repeated calling?

I read a very interesting article by Mike Brick. The discussion of changing propensities in section 7 on pages 341-342 was particularly interesting. He discusses the interpretation of changes in average estimated response propensities over time. Is it due to changes in the composition of the active sample? Or, is it due to within-unit decreases in probability caused by repeated application of the same protocol (i.e. more calls)?

To me, it seems evident that people's propensity to respond do change. We can increase a person's probability of response by offering an incentive. We can decrease another person's probability by saying "the wrong thing" during the survey introduction.

But the article specifically discusses whether additional calls actually change the callee's probability of response. In most models, the number of calls is a very powerful predictor. Each additional call lowers the probability of response. Brick points out that there are two interpretations of that. Either each call reduces the probability for each case, or as the mixture of active cases shifts toward a larger proportion of more difficult cases the average probability declines.

In this case, I thought the latter explanation was more likely. In fact, a paper I wrote on estimating contact probabilities at the household-level makes the assumption (which is also sometimes wrong) that the household probability of contact is fixed within any window and can be more precisely estimated with repeated trials. I explicitly argued that the average "8th call" probability of contact was not useful for planning a strategy for calling any household as it is simply the average contact probability for a set of difficult to contact cases.

I thought the article did a good job of outlining this controversy in a very clear way.

### "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…