Dear Mr Rubin,
Many thanks for your response. I think you have understand what is my problem.
I agree with you. A multivariate test would be probably better. Perhaps can I
use Lisrel since this software allows the possibility to compare to independant
matrices of correlations. In this case, how make the comparison (I have a recent
version of Lisrel, but I do not master it very well)?
With regard to your response about chi-squared test, I understand the first
condition, that is, if my comprehension is correct, the distributions of
correlations of sample A and B must be normal. On the other hand, I do not
understand what you say in the following paragraph "Also, and you did not ask
this question, but indicated
it, the closeness of the various sample correlation coefficients are highly
dependent, so one could not use the chi-squared test on the set of numbers." Can
you give more explanations on this point?
Many thank for your help
Bruno Facon
Herman Rubin a écrit :
> This is correct IF you have the correct distribution of
> the correlation coefficient; this is NOT easy, and is
> very highly dependent on normality.
>
> Also, and you did not ask this question, but indicated
> it, the closeness of the various sample correlation
> coefficients are highly dependent, so one could not
> use the chi-squared test on the set of numbers.
>
> One would be much safer to use covariances, rather
> than correlations. They are still quite dependent,
> but not as much, but their distributions are still
> dependent on normality. If one wants to test the
> equality of covariance matrices under normality,
> the Wilks-Lawley test properly takes into account
> the dependence. This should be found in multivariate
> texts.
>
> --
> This address is for information only. I do not claim that these views
> are those of the Statistics Department or of Purdue University.
> Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
> [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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