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Re: st: RE: Measures of association for a small sample
From
"Roger B. Newson" <[email protected]>
To
[email protected]
Subject
Re: st: RE: Measures of association for a small sample
Date
Wed, 11 Jan 2012 11:43:03 +0000
Sorry, the -somersd- command would be even better as
somersd X Y, taua transf(z) tdist
which gives marginally fatter confidence intervals which are PROBABLY
more realistic.
Best wishes
Roger
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: [email protected]
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
Opinions expressed are those of the author, not of the institution.
On 11/01/2012 11:37, Roger B. Newson wrote:
I would second the recommendation of -ktau-, but would be less keen on
-spearman-. The Daniels permutational limit theorem is a version of the
Central Limit Theorem that works very quickly for Kendall's tau-a but
not so quickly for Spearman's rho. For Kendall's tau-a with continuous
data, the null distribution is almost indistinguishable even at N=8. See
Kendall and Gibbons (1990).
Of course, if you want a confidence interval for Kendall's tau-a instead
of just a P-value, then you can use the -somersd- package, downloadable
from SSC. This should produce sensible results for N=18. As in:
somersd X Y, taua transf(z)
which gives an asymmetric confidence interval for Kendall's tau-a, using
the delta-jackknife method and the Normalizing and variance-stabilizing
Fisher z-transform.
I hope this helps.
Best wishes
Roger
References
Kendall, M. G., and J. D. Gibbons. 1990. Rank Correlation Methods. 5th
ed. Oxford, UK: Oxford University Press.
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: [email protected]
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
Opinions expressed are those of the author, not of the institution.
On 10/01/2012 23:01, Steve Samuels wrote:
I believe that Francisco used the word "population" in a loose sense,
because he didn't realize that it has a technical meaning in
statistics. I think he means "sample". To solve his problem I suggest
-spearman- or -ktau-.
Steve
On Jan 10, 2012, at 10:31 AM, Lachenbruch, Peter wrote:
If you have the entire population, why do you need significance tests?
Isn't the measure sufficient?
________________________________________
From: [email protected]
[[email protected]] On Behalf Of Francisco Rowe
[[email protected]]
Sent: Tuesday, January 10, 2012 4:35 AM
To: [email protected]
Subject: st: Measures of association for a small sample
Hi,
Sorry for taking advantage of statalist for this -I am trying to
measure the association between two variables with a reduced number of
observations (13) which constitutes my entire population.
I have utilised pairwise correlation coefficients (pwcorr) and
regression using an Iteratively Reweighted Least Squares (IRLS)
estimation (rreg) (on cross-sectional data). However, given some of
the assumptions of these measures, the results can be questioned. For
this reason, I would like to implement some additional tests or
measures on my data.
Would it be possible to have some guidance on this?
Are regressions based on IRLS useful in this context?
Which non-parametric measure can it be useful?
Thanks in advance.
Francisco.
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