Skip to main content

More on changing response propensities

I've been thinking some more about this issue. A study that I work on monitors the estimated mean response propensities every day. The models are refit each day and the estimates updated. The mean estimated propensity of the active cases for each day is then graphed. Each day they decline.

The study has a second phase. In the second, phase, the response probabilities start to go up. Olson and Groves wrote a paper using these data. They argue that the changed design has changed the probabilities of response. I agree with that point of view in this case.

But I also recently finished a paper that looked at the stability of the estimated coefficients over time from models that are fit daily on an ever increasing dataset. The coefficients become quite stable after the first quarter. So the increase in probabilities in the second phase isn't due to changes in the coefficients.

The response probabilities we monitor don't account for the second phase (there's no predictor for that). They are based on call records.  So how does the propensity go up? More calling should only decrease the probability of response... unless some other things change. My hypothesis is that cases started to make more appointments in the second phase than before. There was more contact. In general, things that are evidence of an increasing probability of response and are also in the model started to happen. At some point, I'd like to look at the specifics of that.


Popular posts from this blog

"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…