# Deviation from sampling plan

I have data coming from a sample that, for various reasons, don't follow the original sampling plan. Trying to calibrate this sample seems to be very difficult because deviations are hard in some cases.

At first the design aimed to include a number of small regions but not all of them have participated in the study. If we consider bigger areas we have data from all of them but the distribution in the sample is very different from the population (even for post-calibration techniques).

As we have a large amount of data, would it be a good solution to draw a random subsample from this dataset following the probabilities observed in the population by different characteristics?

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What exactly does it mean that the "deviations are hard in some cases"? –  gung Jul 22 '12 at 19:39
I think we need a bit more information to answer your question. For example, what are the main of the "various reasons" it doesn't follow the original plan. Also, exactly what do you mean by calibrate - this has a narrow technical term in survey analysis, but your use suggests that perhaps you might mean post-stratification weighting to population. –  Peter Ellis Jul 22 '12 at 20:06
Thanks for your comments. I'll try to explain better. At first the design try to get results for small regions and not all of them have been participated in the study. If we consider bigger areas we have data from all of them but the distribution in the sample is very different from the population (even for post-calibration techniques). Could I extract a subsample in these circumstances? –  Adolfo Jul 22 '12 at 20:46
Thanks @Adolfo - I've had a go at answering, and also edited your question to include the information your comment. Have a check that I haven't scrambled your meaning. –  Peter Ellis Jul 23 '12 at 7:31