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Thread: Correcting standard errors for changing sample size

  1. #1
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    Correcting standard errors for changing sample size

    Dear all,

    I have a problem I cannot get my head around. I have 22 point estimates and standard errors calculated for the costs associated with a social programs that starts in January (month 1 and goes on for 22 months). Each month new participants joing the program.
    The data structure looks as follows (it is not the real data though):

    point estimate standard error month observations (N)/participants in the program
    148.05 20.08 1 (january) 355
    70.98 2.66 2 (febraury) 799
    -140.07


    etc.
    30.54


    etc
    3
    (march)


    1500



    Now, my results show that the effect of the program are significant during the first 5 months but then are no longer. And my guess is that it is due to the changing population size. I imagine I would need some kind of weighting to compare a result I obtained with 1500 observations and one with 355 to reach an overall evaluation of whether the effects are globally significant over the 22 months.
    Does anyone have an idea with respect to this problem? Any help is appreciated,
    kind regards
    Filomena
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  2. #2
    MHF Contributor
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    Re: Correcting standard errors for changing sample size

    Hey Filomena.

    What sort of assumptions do you place on the different data sets?

    In short - you will need to get a distribution for each group of observations and see how similar/different they are.

    If they are really different then you will need to treat it that way. If they are the same then data can be more easily aggregated.

    The co-variance term may be especially applicable here.
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  3. #3
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    Re: Correcting standard errors for changing sample size

    Hello Chiro,

    thanks for your answer, the only thing I know ist that some of those who enter at a specific time say month 2 will remain some months (or all months) in the saple, however, other will exit and other participants agiain will stay till the end.

    Thanks,
    Filomena
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