Re: st: ttest or xtmelogit?

[Steven Samuels <sjhsamuels@earthlink.net>]

Not mentioned in -transint- is the variance-stabilizing property of  
the angular transformation: it has asymptotic variance 1/4n, which is  
not a function of p (Anscombe, 1948). If the observed proportion is r/ 
n, Anscombe showed that the arcsine of [(r + 3/8)/(n + 3/4)]^.5  is  
even better at stabilizing the variance, for moderate sample size.  
The second version has variance 1/(4n + 2).

The arcsine-transformation used to be recommended because transformed  
proportions  could be analyzed via standard ANOVA programs.  I once  
found it useful in a variance components analysis. The 'error'  
variance was a mixture of a between-sample and within sample  
(binomial) variance. With the arcsine transformation, I could  
subtract out the part attributable to binomial variation.

-Steve

FJ Anscombe 1948. The transformation of Poisson, Binomial, and  
negative-binomial data. Biometrika 35:246-254
On Mar 10, 2008, at 6:02 PM, Nick Cox wrote:


> By arcsin I guess you mean the angular transformation (arcsine of  
> square
> root).
> Its use seems to have faded dramatically in recent years.
>
> Tukey showed that this is very close to p^0.41 - (1 - p)^0.41. That
> makes it weaker
> than the logit. My guess is that it would be an unusual dataset in  
> which
> the angular
> was much better than leaving data as is and also much better than the
> logit. It could happen,
> but it seems to be rare.
>
> The Tukey reference is given in -transint- from SSC.
>
> Nick
> n.j.cox@durham.ac.uk
>
> David Airey
>
> Maybe I should not have said it was pilot data! I won't disagree, but
> when cluster number is too small (< 20) to invoke xtgee or xtmelogit
> on the observed yes/no data, or glm on the summary statistics with
> binomial family and logit link, what do you do? It seems to me there
> is a sample size between 10 and 30 clusters of yes/no data that may be
> better suited to some of the older approaches like arcsin transformed
> proportions and then ttest or ANOVA/regress. I guess that was my
> question.
>
>
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