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Showing posts from October, 2017

The dose matters too...

Just a follow-up from my previous post on mixed-mode surveys. I think that one of the things that gets overlooked in discussions of mixed-mode designs is the dosage of each mode that is applied. For example, how many contact attempts under each mode? It's pretty clear that this matters. In general, more effort leads to higher response rates and less effort leads to lower response rates. But, it seems that sometimes when we talk about mixed-mode studies, we forget about the dose. We wrote about this idea in Chapter 4 of our new book on adaptive survey design . I think it would be useful to keep this in mind when describing mixed-mode studies. It might be these other features, i.e. not the mode itself, that account for differences between mixed-mode studies. At least in part.

Is there such a thing as "mode"?

Ok. The title is a provocative question. But it's one that I've been thinking about recently. A few years ago, I was working on a lit review for a mixed-mode experiment that we had done. I found that the results were inconsistent on an important aspect of mixed-mode studies -- the sequence of modes. As I was puzzled about this, I went back and tried to write down more information about the design of each of the experiments that I was reviewing. I started to notice a pattern. Many mixed-mode surveys offered "more" of the first mode. For example, in a web-mail study, there might be 3 mailings with the mail survey and one mailed request for a web survey. This led me to think of "dosage" as an important attribute of mixed-mode surveys. I'm starting to think there is much more to it than that. The context matters  a lot -- the dosage of the mode, what it may require to complete that mode, the survey population, etc. All of these things matter. Still, we

Should exceptions be allowed in survey protocol implementation?

I used to work on a CATI system (DOS-based) that allowed supervisors to release cases for calling through an override mechanism. That is, the calling algorithm had certain rules that kept cases out of the calling queue at certain times. The main thing was if something had been called and was a "ring-no-answer," then the system wouldn't allow it to be called (i.e. placed in the calling queue) until 4 hours had passed. But supervisors could override this and release cases for calling on a case-by-case basis. This was handy -- when sample ran out, supervisors could release more cases that didn't fall within the calling parameters. This kept interviewers busy dialing. Recently, I've started to think about the other side of such practices. That is, it is more difficult to specify the protocol that should be applied when these exceptions are allowed. Obviously, if the protocol is not calling a case less than four hours after a ring-no-answer, then the software explicit

Future of Responsive and Adaptive Design

A special issue of the Journal of Official Statistics on responsive and adaptive design recently appeared. I was an associate editor for the issue and helped draft an editorial that raised issues for future research in this area. The last chapter of our book on Adaptive Survey Design also defines a set of questions that may be of issue. I think one of the more important areas of research is to identify targeted design strategies. This differs from current procedures that often sequence the same protocol across all cases. For example, everyone gets web, then those who haven't responded to  web get mail. The targeted approach, on the other hand, would find a subgroup amenable to web and another amenable to mail. This is a difficult task as most design features have been explored with respect to the entire population, but we know less about subgroups. Further, we often have very little information with which to define these groups. We may not even have basic household or person