Estimating Response Probabilities for Surveys

I recently went to a workshop on adaptive treatment regimes. We were presented with a situation where they were attempting to learn about the effectiveness of a treatment to help with a chronic condition like addiction to smoking. The treatment is applied at several points over time, and can be changed based on changes in the condition of the person (e.g. they report stronger urges to smoke). In this setup, they can learn effective treatments at the patient level.

In surveys, we only observe successful outcomes one time. We get the interview, we are done. We estimate response propensities by averaging over sets of cases. Within in any set, we assume that each person is exchangeable. Not by observing response to multiple survey requests on the same person.

Even panel surveys are only a little different. The follow-up interviews are often only with cases that responded at t=1. Even when there is follow-up with the entire sample, we usually leverage the fact that this is follow-up to a familiar survey.

I'd like to see experiments where multiple survey requests are made to the same units. It would be interesting to see if you could validate model results that way. Sadly, you might need a lot of survey requests per case (n=20+). But, hey, it's all in the name of science.

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Tailoring vs. Targeting

One of the chapters in a recent book on surveying hard-to-reach populations looks at "targeting and tailoring" survey designs. The chapter references this paper on the use of the terms among those who design health communication. I thought the article was an interesting one. They start by saying that "one way to classify message strategies like tailoring is by the level of specificity with which characteristics of the target audience are reflected in the the communication." That made sense. There is likely a continuum of specificity ranging from complete non-differentiation across units to nearly individualized. But then the authors break that continuum and try to define a "fundamental" difference between tailoring and targeting. They say targeting is for some subgroup while tailoring is to the characteristics of the individual. That sounds good, but at least for surveys, I'm not sure the distinction holds. In survey design, what would constitute ...

"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 ...

What is Data Quality, and How to Enhance it in Research

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