Skip to main content

What if something unexpected happens?

I recently finished teaching a short course on responsive survey design. We had some interesting discussions. One of the things that we emphasized was the pre-planned nature of responsive design. We contrast responsive design with ad hoc changes that a survey might make in response to unanticipated problems. The reasoning is that ad hoc changes are often done under pressure and, therefore, are likely to be less than optimal -- that is, they might be implemented too late, cost too much, or better options might not be considered. Further, it's hard to replicate the results when decisions are made this way.

Some of the students seemed uneasy about this definition. In part, I think this was because there was a sort of implication that one shouldn't make ad hoc changes. That really wasn't our message. Our point was that to be responsive design, it needs to be pre-planned. We didn't mean that if unanticipated problems arise, it would be better to do nothing. In this sense, responsive design might be a goal and not all surveys live up to that goal. I'm not too proud to admit having been in an ad hoc meeting or two.

I'll take this opportunity to mention that we have a new book out (with Barry Schouten and Andy Peytchev) on Adaptive Survey Design that looks at the question of what are adaptive and responsive survey designs.

Comments

Popular posts from this blog

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 forms of d

An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea. Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample. Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assu