### There are call records, and then there are call records...

In my last post, I talked about how errors in call records might lead to bad things. If these errors are biasing (i.e. interviewers always underreport and never overreport calls -- which seems likely), then adjustments based on call records can create (more) bias in estimates. I pointed to the simulation study that Paul Biemer and colleagues carried out. They used an adjustment strategy that used the call number.

There are other ways to use the data from calls. For instance, if I'm using logistic regression to estimate the probability of response, I can fit a model with a parameter for each call. Under that approach, I'm not making an assumption about the relationship between calls and response. It's like the Kaplan-Meier estimator in survival analysis. If there is a relationship, then I can fit a logistic regression model with fewer parameters. Maybe as few as one if I think the relationship is linear. That smooths over some of the observed differences and assumes they are just sampling error. Such an approach might mitigate the impact of errors in call records.

We have also sometimes created categories out of the number of calls. This definitely smooths over some of the errors in underreporting,  but requires the assumption that cases grouped together are essential the same. This might seem kind of odd -- it's like saying that 2 calls is the same as 3 calls if I group them together. But given that those two calls might have been at good times, while one or two of the three calls were at bad times, it doesn't seem so odd.

We have also tried taking the natural logarithm of the call numbers. This one makes intuitive sense to me. Under this transformation, the difference between 1 and 2 calls is much bigger than the difference between 12 and 13 calls.

Of course, I'd prefer nice, clean call records. But there may be some methods that help mitigate the impact of the mess.

### "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…