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 characteristics in many surveys. Finally, even with demographic variables, there is still a lot of heterogeneity in outcomes. Not all young people will do web surveys. Often, paradata based on effort are the most powerful predictors in response propensity models. Which leads us back to sequential procedures where changes are based on paradata.
However, these aren't showstoppers. We still have the ability to explore these targeted designs. It might be that we start with easier situations, such as panel surveys, where we do have a lot of data on persons. But we need to develop more evidence and experience. So, good news, there is more research to do.
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 characteristics in many surveys. Finally, even with demographic variables, there is still a lot of heterogeneity in outcomes. Not all young people will do web surveys. Often, paradata based on effort are the most powerful predictors in response propensity models. Which leads us back to sequential procedures where changes are based on paradata.
However, these aren't showstoppers. We still have the ability to explore these targeted designs. It might be that we start with easier situations, such as panel surveys, where we do have a lot of data on persons. But we need to develop more evidence and experience. So, good news, there is more research to do.
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