In addition the poster did not tell us what is wrong with a
nonparametric test.
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
On Mon, 2 Aug 2010, Bert Gunter wrote:
My sympathies, but I don't think it's the business of list
contributors to facilitate stupidity.
"Confidence interval for the p-value" is nonsense. You could try
sensitivity analyses via simulation, though.
Cheers,
Bert Gunter
Genentech Nonclinical Biostatistics
On Mon, Aug 2, 2010 at 11:31 AM, wwreith <reith_will...@bah.com> wrote:
I am testing normality on the studetized residuals that are generated after
performing ANOVA and yes I used Levene's test to see if the variances can be
assumed equal. They infact are not, but I have found a formula for determining
whether the p-value for ANOVA will become larger or smaller as a result of
unequal variances and unequal sample sizes. Fortuneately it turns out the
p-value is greater. Despite this the ANOVA test is still significant with
p=.000.
The problem I have is that I am expected, by my client, to find a similiar
formula that states which way the p-value would be pushed by a lack of
normality. Despite numerous citations that ANOVA is robust to departures of
normality my client does not care. They want numerical proof. This lead to
looking for a method for estimating the effects non normality would have on the
p-value for ANOVA. In other words can I build a confidence interval for the
p-value? Hence the error term I am speaking of would be a the margin or error
for p-value confidence interval.
William W. Reith III
Business Analytics
J9 SAC (757)-203-3400 Best Contact From 7:00am-4:00pm
J9 Office (757)-203-3772
Booz Office (757) 466-3253
Mobile (434)-989-7948
________________________________
From: David Winsemius [via R] [ml-node+2310616-1859960724-371...@n4.nabble.com]
Sent: Monday, August 02, 2010 1:33 PM
To: Reith, William [USA]
Subject: Re: Problems with normality req. for ANOVA
On Aug 2, 2010, at 9:33 AM, wwreith wrote:
I am conducting an experiment with four independent variables each
of which
has three or more factor levels. The sample size is quite large i.e.
several
thousand. The dependent variable data does not pass a normality test
but
"visually" looks close to normal so is there a way to compute the
affect
this would have on the p-value for ANOVA or is there a way to
perform an
nonparametric test in R that will handle this many independent
variables.
Simply saying ANOVA is robust to small departures from normality is
not
going to be good enough for my client.
The statistical assumption of normality for linear models do not apply
to the distribution of the dependent variable, but rather to the
residuals after a model is estimated. Furthermore, it is the
homoskedasticity assumption that is more commonly violated and also
greater threat to validity. (And if you don't already know both of
these points, then you desperately need to review your basic modeling
practices.)
I need to compute an error amount for
ANOVA or find a nonparametric equivalent.
You might get a better answer if you expressed the first part of that
question in unambiguous terminology. What is "error amount"?
For the second part, there is an entire Task View on Robust
Statistical Methods.
--
David Winsemius, MD
West Hartford, CT
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