SD1 <- SD2 <- N1 <- N2 <- 5
var.test(SD1*rnorm(N1), SD2*rnorm(N2))For more than two variances, you could use bartlett.test similarly. However, Bartlett's test, and presumably also var.test, is highly sensitive to non-normality. I don't have a citation, but I remember hearing George Box say that Bartlett's test is almost a better test of non-normality than of inhomogeneity of variance. If you needed a citation for that, I would look first at various papers and book sections discussing robustness and Bartlett's test, especially in the index of Box on Quality and Discovery (Wiley, 2000) or his earlier collected works volumes.
This may answer to "independent samples" question. However, we would need to know more about the nature of the dependence to answer the dependent samples question, and a sensible answer to the latter may require untenable assumptions.
hope this helps. spencer graves
Markus Koesters wrote:
Hello,
for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means.
I want to test the variances from dependent and independend samples.
I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ?
Thanks in advance
Markus
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