### Baby and the Bathwater

This post is a follow-up on my last. Since my last post, I came across an interesting article at Survey Practice. I'm really pleased to see this article, since this is a discussion we really need to have. The article, by Koen Beullens and Geert Loosveldt, presents the results of a simulation study on the impact of using different indicators to govern data collection. In other words, the simulate the consequences of maximizing different indicators in data collection. The three indicators are the response rate, the R-Indicator (Schouten, et al., 2009), and the maximal bias (also developed by Schouten et al. 2009). The simulation shows a situation where you would get a different result from maximizing either of the latter two indicators compared to when you maximize the response rate. Maximizing the R-Indicator, for example, led to a slightly lower response rate than the data collection strategy that maximizes the response rate.

This is an interesting simulation. It pretty clearly explores the distortions that can occur when maximizing the response rate is the goal.

However, I don't see it as convincing evidence that we should radically change our data collection procedures. As I mentioned in my last post, I wouldn't want anyone to conclude that lowering response rates is always OK. The problem is certainly more complicated than that.I would contend that we need experimental evidence regarding the impact of using other indicators to guide data collection.

In the first instance, data collection is such a complex activity that it is impossible to describe all the 'essential features' of any design. It's even more difficult to understand the impact of all these choices. Before changing those practices, we should understand the consequences of doing so. We wouldn't want to throw out the baby with the bathwater. In my mind, that requires experimental evidence.

It is also the case that each of the indicators proposed has weaknesses. If the model underlying the R-Indicator is misspecified, this could lead to inefficient or even bias-increasing actions. It would be good to understand when and how this might happen -- and what protections against this we might develop. My view is that this will require a constellation of indicators that tell an underlying story.

The good news is that this would require the work of many survey methodologists.

### "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…