Skip to main content

Sorry I missed you...

This is another post in a series on currently used survey design features that could be "relabeled" as adaptive. I think it is helpful to relabel for a couple of reasons. 1) It demonstrates a kind of feasibility of the approach, and 2) it would help us think more rigorously about these design options (for example, if we think about refusal conversions as a treatment within a sequence of treatments, we may design better experiments to test various ways of conducting conversions).

The design feature I'm thinking of today has to do with a card that interviewers leave behind sometimes when no one is home at a face-to-face contact attempt. The card says "Sorry I missed you..." and explains the study and that we will be trying to contact them.

Interviewers decide when to leave these cards. In team meetings with interviewers, I heard a lot of different strategies that interviewers use with these cards. For instance, one interviewer said she leaves them every time, even if they stack up. Others used them less frequently after several failed attempts. In any event, they have the decision rules in their heads. (They also have a lot of "data" about each housing unit and more or less experience with making contact with households.) These rules seem to vary.

I could (and did) imagine an adaptive rule that would say when these cards should be left behind. I fit a model that included a bunch of interactions with the SIMY card and other characteristics of the housing unit. The result was a prediction about when SIMY card helped and when it hurt. I then delivered recommendations to interviewers based on these model estimates. The adaptive rule could be stated as:

1) If the SIMY card increase probability of contact on the next attempt, then leave it.
2) If the SIMY card descreases or doesn't effect the probability of contact on the next attempt, then don't leave it.

Whether this rule works or not, I don't know. The interviewers didn't follow the model recommendations.

Comments

Popular posts from this blog

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

  We often talk about “data quality” or “data integrity” when we are discussing the collection or analysis of one type of data or another. Yet, the definition of these terms might be unclear, or they may vary across different contexts. In any event, the terms are somewhat abstract -- which can make it difficult, in practice, to improve. That is, we need to know what we are describing with those terms, before we can improve them. Over the last two years, we have been developing a course on   Total Data Quality , soon to be available on Coursera. We start from an error classification scheme adopted by survey methodology many years ago. Known as the “Total Survey Error” perspective, it focuses on the classification of errors into measurement and representation dimensions. One goal of our course is to expand this classification scheme from survey data to other types of data. The figure shows the classification scheme as we have modified it to include both survey data and organic f...