There is a bundle of different questions here
on quite different levels.
I'll restrict myself to a few comments:
The Kruskal-Wallis test knows nothing whatsover about
variances; that is, in a strong sense, most of the point
to the test.
Bartlett's test is embedded in the literature and
in programs but I don't think many practical data
analysts pay much attention to it. Like many such
tests, it is likely to be oversensitive to small
differences, especially at large sample sizes.
If you are seriously worried about heteroscedasticity
then either you should be working on transformed scales
or else -oneway- is not really the answer to your
substantive question. I don't think that seeking
an answer that is -oneway- plus some small trick
is the best way to proceed.
In any case, although you say nothing about
your substantive problem, the mention of returns leads me
to suspect a time series context. If that is
true, any P-values coming out of -oneway- are
likely to be pure garbage. Otherwise put, violation
of independence assumptions is a whole heap more
problematic than unequal variances. Nor will Kruskal-
Wallis help in this situation.
Rupert G. Miller's book "Beyond ANOVA" is
a wonderful source in this terrain. It was originally
published by Wiley in 1986 and reissued by Chapman
and Hall in 1997.
Nick
[email protected]
Thomas Erdmann
> comparing ten portfolios of returns using -oneway- ,
> Bartlett's test for
> equal variances always highly rejects the null hypothesis.
>
> 1.) What routines can be used in Stata if the assumptions of ANOVA are
> violated?
>
> 2.) Generally speaking, does the violation of ANOVA
> assumption shift the
> F-test to more conservative results (i.e. tends not to reject H0 of
> equality)?
>
> I am aware that nonparametric tests like the Kruskal-Wallis
> test ( -kwallis-
> , -kwallis2- ) can help with settings where the normality
> assumption of the
> ANOVA is violated, but it still assumes equal variance.
>
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