wuzzy wrote:
> Bruce Weaver <[EMAIL PROTECTED]> wrote in message
>news:<[EMAIL PROTECTED]>...
> > On 20 Apr 2002, Voltolini wrote:
> >
> > > Hi, some of my students are asking me if there are
> > > non parametric versions for these tests:
> > >
> > > 1 - ANOVA two-way.
> > > 2 - ANOVA multifatorial or multi-way.
> > > 3 - Multiple regression.
> > > 4 - Analysis of covariance (ANCOVA).
> > >
> > > Thanks for any help !
> > >
> > >
> > > Voltolini
> > >
> >
> > Do a search for "assumptions of ANOVA 1" at http://groups.google.com/ .
> > In my message, there is a quote from Conover's book on nonparametric stats
> > that addresses this issue.
> >
> > Cheers,
>
> The question is good, there are very few multivariate nonparametric
> techniques:
> As suggested, rank transformations are probably the way to deal with
> this.
>
The rank transform when used in conjunction with factorial ANOVA can produce HIHGLY
INFLATED Type I error rates. Look up articles by Georgia
Thompson in JASA and Shlomo Sawilowsky in Journal of Education Statistics (?) before
resoring to this technique.
cliff blair
>
> I have also wondered if residuals are a way of adjusting and then you
> can use bivariate non-parametrics like kendall's tau, spearman etc.
>
> I don't believe residuals work too well because of the following
> example:
> Correlations
> AS BEANSP AGE
> AS pearson 1.000 1.000 -.129
> Sig. . .000 .000
> N 4074 4074 4074
> BEANSP Pearson 1.000 1.000 -.141
> Sig. .000 . .000
> N 4074 4074 4074
> AGE Pearson -.129 -.141 1.000
> Sig. .000 .000 .
> N 4074 4074 4164
>
> the variable "AS" Is "BEANSP" adjusted for age, and you can see that
> adjustment only reduces confounding by a very minor amount. r=-0.129
> compared to r=-0.141.
> This probably depends on the population/data..
.
.
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