Survey Methods Musings

Another interesting question that I had during the webinar that I recently gave concerned responsive design and surveys with short time frames. I have to say, I mostly work on surveys with relatively long time frames. The shortest data collection that I have worked on in the last few years is about one month. That's not to say that I think responsive design is not relevant for surveys with short field periods. I think it is. If anything, following the prescribed regimen may be more important. A key aspect of responsive design, in my mind, is that the process is pre-planned. The indicators that are monitored, the decision rules for implementing interventions, the interventions, all have to be pre-planned. In a short survey, this is particularly important as their isn't time for developing ad hoc solutions. In a former life, I worked on surveys that had field periods of a day or two. In those studies, there wouldn't have been time to meet, discuss, and decide. Given the s...

I conducted a webinar on responsive design this week. I had several interesting questions. One of these was a question about responsive design and quota sampling.  The question was whether these two approaches are, in fact, different? Of course, there are similarities in that the response process is being controlled -- somewhat -- by the researchers. And this may lead to "allocating" nonresponse to some groups over others. For example, if some group is responding at higher rates, we might allocate resources to the lower responding group. Quota sampling will stop data collection for groups that have reached their quota. There are differences, however. Responsive design attempts to provide balanced response, but doesn't necessarily force that to happen. Further, responsive design is attempting to control the data collection process using a variety of approaches. Quota sampling only has one approach -- stop when the quota is full.  I do worry that there may be a conver...

I've been getting ready to give a webinar on responsive design. I enjoy getting ready for this kind of talk as it gives me an opportunity to think about definitions and concepts. A few years ago, Mick Couper and I had a paper on "responsive vs adaptive" design. My thinking hasn't evolved much since that paper. In preparing the talk, I thought it might be helpful to define responsive design by contrast with ... that which is not responsive design. The contrasts were 1) pre-specified designs, and 2) ad hoc designs. The first category is a design where a pre-specified design is implemented and the results are pretty much as predicted. I personally haven't worked on many surveys like that, but I'm not yet ready to call it a "straw man." The second category is an approach I have seen in action. I sometimes call this approach "shooting from the hip." This is the situation where we start with a pre-specified design, but when it goes off the...

In Groves and Heeringa 's original formulation, responsive design proceeds in phases. They define these phases as: "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." (page 440). These responsive design phases are different than the two-phase sampling defined by Hansen and Hurwitz. Hansen and Hurwitz assumed 100% response so there was no nonresponse bias.  There two-phase sampling was all about minimizing variance for a fixed budget.  Groves and Heeringa, on the other hand, live in a world where nonresponse does occur.  They seek to control it through phases that recruit complementary groups of respondents. The goal is that the nonresponse biases from each phase will cancel each other out. The focus on bias is a new feature relative to Hansen and Hurwitz.  A question in my mind about the phases is how the...

In clinical trials, they have the concept that there is an "existing standard of care." New treatments are compared experimentally to this treatment. I suppose that clinical trials have some issues where informed persons can disagree about the existing standard of care, but there is at least some consensus. I'm wondering what we have for existing standard practice in the administration of surveys? As I think about running experiments, the contrast is usually to the other thing we would normally do. But, that can be ill-defined. For instance, when running experiments in our telephone facility, it was difficult to describe current practice precisely as it involved expert knowledge of the managers adjusting parameters of the calling algorithm. As further evidence that it's difficult to precisely define the essential survey conditions, there are several articles on " house effects ," where the same survey with the same (rough?) specification ends up getting ...

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. Researc...

I've been thinking about responsive design and uncertainty. I know that when we teach sample design, we often treat design parameters as if they were known. For example, if I do an optimal allocation for a stratified estimate, I assume that I know the population element variances for each stratum. The same thing could be said about response rates, which relate to the expected final sample size. Many years ago, the uncertainty might have been small about many of these parameters. But responsive design became a "thing" largely because this uncertainty seemed to be growing. The question then becomes, how do we acknowledge and even incorporate this uncertainty into our designs? Especially responsive designs. It seems that the Bayesian approach is a natural fit for this kind of problem. Although I can't find a copy online, I recall a paper that Kristen Olson and Trivellore Raghunathan presented at JSM in 2005. They suggested using a Bayesian approach to update estimate...