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Web surveys are typical web information systems. As part of the interface of a survey, progress indicators inform the participants about their state of completion. Different studies suggest that the progress indicator in web surveys has an impact on the dropout and answer behaviour of the participants. Therefore, researchers should invest some time in finding the right indicator for their surveys. But the calculation of the progress is sometimes more difficult than expected, especially, in surveys with a lot of branches. Current related work explains the progress computation in such cases based on different prediction strategies. However, the performance of these strategies varies for different surveys. In this position paper, we explain how to compare those strategies. The chosen Root Mean Square Error measure allows to select the best strategy. But experiments with two large real-world surveys show that there is no single best strategy for all of them. It highly depends on the structure of the surveys and sometimes even the best known strategy produces bad predictions. Dedicated research should find solutions for these cases.

The Problem of Finding the Best Strategy for Progress Computation in Adaptive Web Surveys