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RE: st: Bootstrapping question


From   "Ilian, Henry (ACS)" <Henry.Ilian@dfa.state.ny.us>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Bootstrapping question
Date   Wed, 6 Feb 2013 15:53:52 -0500

The sample size 1s 27, which is the largest number the case readers can handle in the amount of time allotted. The population the sample is drawn from is 140. To get a confidence interval for proportion I used Lenth's on-line application, http://homepage.cs.uiowa.edu/~rlenth/Power/. Since the items have different proportions, there are several confidence intervals. Using 50% as the proportion (meaning that for a particular item, 50% of the sample were awarded the highest ordinal rating, and the other 50% were awarded other ratings), I got a margin of error of 17.3%. For a proportion of 70%, the margin of error is 16%, etc.

I'm new to the idea of bootstrapping, but it seemed to be a way to improve the confidence intervals.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: Wednesday, February 06, 2013 2:04 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Bootstrapping question

For once, my line differs slightly from Maarten's.

The crunch is that nowhere did Henry state where his confidence
intervals come from. If they were based on inappropriate assumptions,
bootstrapping may do better. But if the confidence intervals one way
are wide, the expectation is of a similar story from -bootstrap-.

Nick

On Wed, Feb 6, 2013 at 6:55 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Wed, Feb 6, 2013 at 5:28 PM, Ilian, Henry (ACS)  wrote:
>> I am working with samples that result in very large confidence intervals, and there is no way to get larger samples. Therefore bootstrapping is an appealing option.
>
> Unfortunately the bootstrap is not going to help. The large confidence
> intervals mean that there is very little information present in your
> data, and no statistical technique can add information that was not
> present in your data to begin with. So it seems that you will just
> have to live with the very large confidence intervals.
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