Survey Methods Training

Survey Practice devoted the entire current issue to a discussion of training in survey methodology. This is a very useful review of what is currently done and suggestions for the future.

As they observe, survey methodology is a broad discipline that draws upon a diverse set of fields of research. I expect that increasing this diversity would be positive. That is, there are a number of fields of study that would find applications for their methods in the field of survey research.

A couple of key examples include operations research and computer science. Operations research could help us think more rigorously about designing data collection to optimize specified quantities. That doesn't mean we have to pursue one goal. But it would help, or maybe force us to quantify the vague trade offs we usually deal in. The paper by Greenberg and Stokes is an early example. The paper by Calinescu and colleagues is a recent one.

Computer science is another such field. Researchers studying reinforcement learning seek to optimize complex, multi-stage decision problems. These methods have been used to optimize adaptive treatment regimes. I think they may be a natural fit for some survey design problems. For example, the design of mixed mode surveys. Hopefully, we can direct ourselves toward such a future.

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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...

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