We can say that we have doubly censored information in the occurrence of an event when we don’t know the exact time of its occurrence, but only that it may have occurred during a given period. I have tried with Najim Jamal to answer to this question for demography in our paper on Interval-censored event history analysis (1996), but I think that it may be applied to many others social sciences.
For us, we have applied some statistical methods to demographic data artificially doubly censored. Let me give more precise information about the problems we encountered, the methods we used, and the results we get.
These problems occur when we have, for example, a Demographic Panel Survey giving the information obtained at different censuses for the same individuals: in this case we have only the place of residence or occupation at each census to infer migration or job changes between them. In this case the density of events defining the individual’s position may be sufficient for few migrations or job changes to slip through. In another example, we may know current place of residence and current occupation at the time of each vital registered event, as in surveys in the past. In this case, another hypothesis is also necessary to estimate durations of stay from such a panel: the vital events defining the individual’s spatial or social position must be independent of the geographical and or occupational mobility we wish to measure.
We applied these methods to two data sets: a complete retrospective data set and these data artificially interval-censored. You will have also application of these methods in my ResearchGate website: An attempt to analyse individual migration histories from data on place of usual residence at the time of certain vital events (1993); Reconstruire des trajectoires de mobilité résidentielle (1998).
I would like to know if some researchers in ResearchGate have encountered such problems and how they have tried to solve them.
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