### Proxy Y's

My last post was a bit of crankiness about the term "nonresponse bias." There is a bit of terminology, on the other hand, that I do like -- "Proxy Y's." We used this term in a paper a while ago.

The thing that I like about this term, is that it puts the focus on the prediction of Y. Based on the paper by Little and Vartivarian (2005), this seemed like a more useful thing to have. And we spent time looking for things that could fit the bill.

If we have something like this, the difference between responders and the full sample might be a good proxy for bias with the actual Y's. I'm not backtracking here -- it's still not "nonresponse bias" in my book. It's just a proxy for it.

The paper we wrote found that good proxy Y's are hard to find. Still, it's worth looking. And, as I said, the term keeps us focused on finding these elusive measures.

1. I also like the way Andridge and Little (2011) defines proxy variable as way to reduce dimensionality using the prediction of the outcome variable based on a set of auxiliary variables. There I think it's even more explicit the focus on the prediction of Y that you mention.

1. I was thinking mainly of paradata design. That is, designing auxiliary variables that can function as proxy Y's. These are likely to be interviewer observations. But could be other paradata elements.

2. I see, variables such as presence of children at home in NSFG, right? I believe that's a very interesting way to go too. I have suggested that in a survey and they even implemented that, but we didn't use that for anything at the end. They thought that the interviewers observations were not very good, when compared to respondent data. In that sense, I believe we also need more research like what Brady West has been doing in evaluating measurement error in these kind of paradata.

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### "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…