Skip to main content

Responsive Design and Uncertainty

To my mind, a key reason for responsive designs is uncertainty. This uncertainty can probably occur in at least two ways. First, at a survey level, I can be uncertain about what response rate a certain protocol can elicit. If I don't obtain the expected response rate after applying the initial protocol, then I can change the protocol and try a different one.

Second, I can be uncertain about which protocol to apply at the case level. But I know what the protocol will be after I have observed a few initial trials of some starting protocol. For example, I might call a case three times on the telephone with no contact before I conclude that I should attempt the case face-to-face.

In either situation, I'm not certain about which protocol specific cases will get. But I do have a pre-specified plan that will guide my decisions during data collection. There is a difference, though, in that in the latter situation (case level), I can predict that a proportion of cases will receive the second, changed protocol. And in order to optimize, I need to be able to do that. Calinescu et al. make this observation.

In the survey level case, I don't necessarily need to do that, I can optimize in phases. Of course, everyone has to be willing to live with the results -- even if this means obtaining fewer than expected interviews. In some sense, my uncertainty extends to my final product which may differ from that I initially expected.


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...