Progress indicators are used to inform participants of surveys about the degree of completion and the remaining quantity of questions. The main aim of progress indicators is to reduce drop out rates. Theory holds that this is accomplished by allowing participants to estimate the remaining time till completion. Participants might base their estimation on the time invested in relation to the number of completed items and the perceived speed of the progress indicator. If the estimated time till completion is beyond acceptance the participant drops out. A common method displays the number of pages/items completed and the total number of pages/items in the survey. These numbers may be visualized by a graphical bar which either substitutes the numbers or supports them. Instead of the raw pages/items numbers the percentage of completed pages/items may be displayed. These methods can be characterized as static approaches in computing the percentage till completion. When using surveys which implement filter techniques this leads to a serious problem: filters used to skip non-appropriate items for groups of participants cause the progress indicator to jump to completion rather than progress in a smooth way. This behaviour results in unpredictable percentages in the view of participants and thus ruins the advantages of predictable time estimations. To cope with this problem a number of computing methods were developed which can be characterized as dynamic approach. With an algorithm easy to implement the overall pages or items missing till completion are estimated for the participant on every page displayed together with an estimated total number of pages/items in the survey. These numbers vary in accordance with the filter path of the survey resulting in a smooth completion of the progress indicator. Three dynamic approaches are shown which address the problem of static approaches. A web-experiment conducted with members of the Rhein-Neckar-panel at ZUMA is presented. The experiment included a control group without progress indicator and demonstrates the impact of static vs. dynamic approaches and the perceived speed of the progress indicator on the drop out.