Skip to main content

'Design Phases' and 'Responsive Design'

One of the sticking points that I've had with reconciling 'adaptive designs' and 'responsive designs' has been Groves and Heeringa's description of responsive design which includes the notion of 'design phases.' Their definition says: "A design phase is a time period of a data collection during which the same set of sampling frame, mode of data collection, sample design, recruitment protocols and measurement conditions are extant."

This definition didn't seem to fit too well with the notion of adaptive design. In adaptive design, the treatments are tailored to the individual. In the survey context, a treatment can be an incentive, additional calls, etc. The tailoring variable is a time-varying variable -- that is, it changes during the field period. When a persons value on the tailoring variable changes, their treatment changes as well. Under this approach, the design phase is a person-level attribute. One person can be in 'phase 1' while another is in 'phase 2'. There is no time period which can claim a common treatment protocol for all units.

I suppose we could say that the notion of design phase is not an essential aspect of responsive design. If we ignore it, then I'm comfortable saying that adaptive designs are a subset of responsive designs.

We might also redefine phase to allow for individuals to be in different design phases at the same point in time. Again, under this relaxed definition, I'm comfortable describing adaptive designs as a subset of responsive designs.
Labels:

Comments

Post a Comment

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 ...
36 comments
Read more

"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 ...
7 comments
Read more

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...
2 comments
Read more