### What would a randomized call timing experiment look like?

It's one thing to compare different call scheduling algorithms. You can compare two algorithms and measure the performance using whatever metrics you want to compare (efficiency, response rate, survey outcome variables).

But what about comparing estimated contact propensities? There is an assumption often employed that these calls are randomly placed. This assumption allows us to predict what would happen under a diverse set of strategies -- e.g. placing calls at different times.

Still, this had me wondering what a really randomized experiment would look like. The experiment would be best randomized sequentially as this can result in more efficient allocation. We'd then want to randomize each "important" aspect of the next treatment. This is where it gets messy. Here are two of these features:

1. Timing. The question is, how to define this. We can define it using "call windows." But even the creation of these windows requires assumptions... and tradeoffs. The key assumption about a window is that contact probabilities within any window are homogenous. We can make very wide windows (i.e. windows with big chunks of time). These windows will have more data in each window. But the assumptions that contact probabilities are homogenous within any window seems plausible. If we make narrow windows, then the homogeneity assumption is more plausible. But we have less data in each window. Imagine estimating contact probabilities across 24*7=168 windows, one for each hour of the week!

2. Lag. How much time between each call? Most call centers and field operations don't do a great job controlling this dimension. Some cases may have huge lags. It may be hard to explain way. For some reason, they fall to the "bottom of the pile" and don't get called very frequently. Again, what are the appropriate lags?

So, small studies have very little ability to estimate a large number of windows and/or a large number of lags. Let alone constrain production to true randomization of these features. Still, for the sake of methods research, it might be fun to try this.

### "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…