Skip to main content

Quantity becomes Quality

A big question facing our field is whether it is better to adjust data collection or do post-data collection adjustments to the data in order to reduce nonresponse bias. I blogged about this a few months ago. In my view, we need to do both.

I'm not sure how the argument goes that says we only need to adjust at the end. I'd like to hear more of that. In my mind, it must be an assumption that once you condition on the frame data, the biases disappear and that assumption is valid at all points during the data collection. That must be a caricature -- which is why I'd like to hear more of the argument from a proponent of the view.

In my mind, that assumption may or may not be true. That's an empirical question. But it seems likely that at some point in the process of collecting data, particularly early on, that assumption is not true. That is, the data are NMAR, even when I condition on all my covariates (sampling frame and paradata). Put another way, in a cell adjustment framework, responders and nonresponders within cells have different means.

At some point, however, there may be a shift. As the data accumulate (quantitative change), the mechanism may shift (qualitative change) from NMAR to MAR (or less NMAR, errr, if there is such a thing). I think that must be an empirical question. It would be nice to have some gold standard studies to understand this.

I further speculate that such a shift (from NMAR to MAR) is more likely to occur in a controlled process than in a relatively uncontrolled one. I say that because I have been thinking about adaptive design as an attempt to place control on a process with a lot of variation, much of it coming from interviewers.

Comments

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…