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Attrition in Designs that use Frequent Measurement

I saw this paper recently that talked about how to measure and evaluate nonresponse to surveys that use short, frequently-administered instruments ("measurement-burst survey").

I've been working on a problem with data like these for a while. A complication was that the questionnaire changed based upon the intervals between measurements. For example, questions might begin, "Since you last completed this survey..." or "in the last two weeks..." depending upon the situation. Plus, panel members could choose to respond at different intervals, even though they were asked to respond at a specified interval.

This made for a complex pattern of missing data. I ended up defining attrition in several ways.  The most useful was to lay out a grid over time. The survey was designed to be taken weekly, so I looked at each week over the time period to see if any reporting occured. This allowed me to how many cells in the grid were missing.

But even that wasn't very satisfying. In this study, it's reasonable to assume that someone who responds every other week gives you more information than someone who responds for the first half of the data collection period. Now I'm looking at imputation as a way to measure the relative information content across different patterns of missing data. This uses all the observed data an looks at how much information was "lost" for each pattern.

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