Re: Antwort: Re: Antwort: Re: st: Multicollinearity in panel data

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 01:02 PM 2/9/2011, Maarten buis wrote:
> --- On Wed, 9/2/11, Justina Fischer wrote:
> > From my own experience, it works for age, age squared and age
> > cubed (all not demeaned), if some people are sufficiently
> > 'aged' in the data
>
> I like centering my variables, not necessarily at the mean, but
> at least such that 0 falls within or near the observed range of
> my variables. That way the constant would be the expected value
> of the outcome variable for some typical individual as defined
> by the way I centered my variables, and I usually have a pretty
> good idea of what that should be. This saved me couple of times
> from some pretty nasty coding mistakes... Also this gives a
> natural way to introduce in the text a baseline against which
> one can compare the size of the effects. This can help when
> discussing what effects are "big" or "small". Admittedly, this
> is more a literary trick than a statistical one, but every
> little bit helps.
One problem with using the mean is that it will differ from sample to 
sample. It might even vary within your own analyses, if differing 
amounts of missing data cause your Ns to differ. I think the most 
critical thing is picking a value so that zero is substantively 
meaningful.  So, for example, if you subtract 12 from education, you 
know that a score of zero now corresponds to high school graduate. 
Or,if gpa ranges from 0 to 4, subtracting 3 means that a B student 
has a score of 0. This might be especially appealing if the means 
were close to these values anyway, e.g. 11.7 years of education or 3.1 gpa.

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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