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Re: st: Bootstrapping question
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: Bootstrapping question
Date
Thu, 7 Feb 2013 09:18:14 +0000
This focuses on intervals for a single (binomial) proportion. Henry's
question was about ordinal variables.
At a wild guess, he wants simultaneous confidence intervals. It also
sounds as if some of his potential outcomes have observed zeros.
Nick
On Thu, Feb 7, 2013 at 3:24 AM, Steve Samuels <[email protected]> wrote:
>
> Lenth's site states that he use the "exact", presumably Clopper-Pearson,
> intervals, which is known to be conservative. But this is not
> what Stata computes. Since a 50% sample proportion is not possible with
> n = 27, the closest one can get to 50% is with k = 13 or 14 events. I'm
> not sure what Lenth's applet shows (I don't have Java enabled), but Stata
> does not show a 17.3% margin of error, rather a number closer to 20%
>
> . cii 27 13
> -- Binomial Exact --
> Variable | Obs Mean Std. Err. [95% Conf. Interval]
> ----------+---------------------------------------------------------------
> | 27 .4814815 .096159 .2866725 .6805035
>
> . cii 27 14
> -- Binomial Exact --
> Variable | Obs Mean Std. Err. [95% Conf. Interval]
> ----------+---------------------------------------------------------------
> | 27 .5185185 .096159 .3194965 .7133275
>
> You could have skipped the trip to Length's site and used Stata's own -cii- command
> with the Wilson intervals, recommended for n < 40 (Brown et al, 2008).
>
> . cii 27 13, wilson
> ------ Wilson ------
> Variable | Obs Mean Std. Err. [95% Conf. Interval]
> ----------+---------------------------------------------------------------
> | 27 .4814815 .096159 .3074323 .6601438
>
> . cii 27 14, wilson
> ------ Wilson ------
> Variable | Obs Mean Std. Err. [95% Conf. Interval]
> ---------+---------------------------------------------------------------
> | 27 .5185185 .096159 .3398562 .6925677
>
> Brown, L. D., T. T. Cai, and A. DasGupta. 2001. Interval estimation for
> a binomial proportion. Statistical Science 16: 101–133
>
> Steve
>
> Steven J Samuels
> Consulting Statistician
> 18 Cantine's Island
> Saugerties NY 12477
> Voice: 845-246-0774
>
> On Feb 6, 2013, at 8:08 PM, Nick Cox wrote:
>
> I don't think I can add usefully to my previous comments. Saying that
> you used a particular program does not convey much to me. What does
> "improve the confidence intervals" mean?
>
> Nick
>
> On Wed, Feb 6, 2013 at 8:53 PM, Ilian, Henry (ACS)
> <[email protected]> wrote:
>> 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: [email protected] [mailto:[email protected]] On Behalf Of Nick Cox
>> Sent: Wednesday, February 06, 2013 2:04 PM
>> To: [email protected]
>> 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 <[email protected]> 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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