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

Assessment of Maching Learning Classifiers

I heard another interesting episode of the Data Skeptic podcast . They were discussing how a classifier could be assessed (episode 121). Many machine learning models are so complex that a human being can't really interpret the meaning of the model. This can lead to problems. They gave an example of a problem where they had a bunch of posts from two discussion boards. One was atheist and the other board was composed of Christians. They tried to classify each post as being from one or the other board. There was one poster who posted heavily on the Christian board. His name was Keith. Sadly, the model learned that if the person who was posting was named Keith, then they were Christian. The problem is that this isn't very useful for prediction. It's an artifact of the input data. Even cross-validation would eliminate this problem. A human being can see the issue, but a model can't. In any event, the proposed solution was to build interpretable models in local areas of t...

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 ...

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...