Setting an Appointment for Sampled Units... Without their Assent

Kreuter, Mercer, and Hicks have an interesting article in JSSAM. In a panel study, the Medical Expenditure Panel Survey (MEPS). They note my failed attempt to deliver recommended calling times to interviewers. They had a nifty idea... preload the best time to call as an appointment. Letters were sent to the panel members announcing the appointment.

Good news. This method improved efficiency without harming response rates. There was some worry that setting appointments without consulting the panel members would turn them off, but that didn't happen.

It does remind me of another failed experiment I did a few years ago. Well, there wasn't an experiment, just a design change. We decided that it would be good to leave answering machine messages on the first telephone call in an RDD sample. In the message, we promised that we would call back the next evening at a specified time. Like an appointment. Without experimental evidence, it's hard to say, but it did seem to increase contact rates slightly in this ongoing survey. However, the telephone facility hated it! It stacked up the calling algorithm with tons of appointments. That was the "failure" that I mentioned earlier.

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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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