It seems odd to say, but "Responsive Design" has now been around for a while. Groves and Heeringa published their paper in 2006. The concept has probably been stretched in all directions at this point.
I find it helpful to back to the original problem statement: we design surveys as if we know the results of each design decision. For example, we know what the response rate will be given a certain design (mode, incentive, etc. -- the "essential conditions"). How would we act if we had no idea about the results? We would certainly expend some resources to gain some information.
Responsive design is built upon this idea. Fortunately, in most situations, we have some idea about what the results might be, at least within a certain range. We experiment within this range of design options in order to approach an optimal design. We expend resources relatively inefficiently in order to learn something that will improve the design of later phases.
I've seen people working in the area of Machine Learning addressing a similar problem. They have to address this question of the value of exploring design options ("policies," in their terminology). What is the value of exploring policies (i.e. gaining information) relative to maximizing the reward under known policies (using all your resources for the design that is assumed to be best)? It might be useful to approach responsive design from this perspective.
I find it helpful to back to the original problem statement: we design surveys as if we know the results of each design decision. For example, we know what the response rate will be given a certain design (mode, incentive, etc. -- the "essential conditions"). How would we act if we had no idea about the results? We would certainly expend some resources to gain some information.
Responsive design is built upon this idea. Fortunately, in most situations, we have some idea about what the results might be, at least within a certain range. We experiment within this range of design options in order to approach an optimal design. We expend resources relatively inefficiently in order to learn something that will improve the design of later phases.
I've seen people working in the area of Machine Learning addressing a similar problem. They have to address this question of the value of exploring design options ("policies," in their terminology). What is the value of exploring policies (i.e. gaining information) relative to maximizing the reward under known policies (using all your resources for the design that is assumed to be best)? It might be useful to approach responsive design from this perspective.
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