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.

Comments

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...

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