Thanks for your comments, David and Bert.
The best would be to provide an example. Let's say we have a dataset like
this one:
IDEmployee Company OU CountViewPortal CountLogin TimeOnTask Performance
1 Company1 Company1.OU1 21 33 627.8 4.3
2 Company1 Company1.OU2 45 54 34.8 2.3
3 Company2 Company1.OU1 23 33 3.8 1.0
4 Company2 Company1.OU1 34 12 44.8 2.3
5 Company2 Company1.OU2 55 22 55.8 4.5
6 Company2 Company1.OU3 45 44 34.8 3

I want to see if there is correlation between CountViewPortal and
Performance. Moreover, I'd like to reveal the influence of
CountViewPortal+TimeOnTask on Performance.
However, I expect that employees within a OU, and than a Company have
similar behavior. Thus, I'll have 3 levels -> employee, OU, Company. In R,
I would do something like this:
randomInterceptCount <- lme(Performance ~ CountViewPortal, data=analysis,
random=~1|OU/Company1, method="ML")

But, then the point is that CountViewPortal, CountLogin and TimeOnTask are
non-normally distributed. I guess that my question is, what should I do in
case of non-normal distribution?

I really appreciate your help. Thanks again!
Srecko


On Mon, Sep 30, 2013 at 5:14 PM, David Winsemius <dwinsemius@comcast.net>wrote:

>
> On Sep 30, 2013, at 3:22 PM, srecko joksimovic wrote:
>
> > I thought so, but then I found this:
> > "Normality
> > The assumption of normality states that the error terms at every level
> of the model are normally distributed"
> > maybe I misinterpreted something.
>
> Notice that it is the _error_terms_ that are to be normally distributed,
> not the data itself. One might even infer that "normally distrited data
> might be suspect because the "correct distribution should be a mixture of
> normals. Since the errors never are going to fit on a straight line on a QQ
> plot, the real question is "how far from Normal" and what the impact might
> be on the quantities being estimated.
>
> --
> David.
> >
> >
> > On Mon, Sep 30, 2013 at 3:06 PM, David Winsemius <dwinsemius@comcast.net>
> wrote:
> >
> > On Sep 30, 2013, at 2:50 PM, srecko joksimovic wrote:
> >
> > > I have an example of multilevel analysis with 3 levels, but data are
> > > non-normally distributed. In case of normal distribution, I would
> perform
> > > multilevel linear analysis using lme function, but what should I do in
> case
> > > of non-normal distribution?
> > >
> >
> > But normal distribution is not a requirement for linear models. Please
> review your theory.
> >
> > > thanks,
> > > Srecko
> > >
> > >       [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > R-help@r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-help
> > > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > > and provide commented, minimal, self-contained, reproducible code.
> >
> > David Winsemius
> > Alameda, CA, USA
> >
> >
>
> David Winsemius
> Alameda, CA, USA
>
>

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