Thanks Chris,

Yeah, I have been thinking, why not just use SD instead of CV.  However,
can't SD be a function of the mean so that if you have a larger mean you
probably will have a larger SD.  Doesn't CV scale SD to the mean so that
you don't have that variation among vastly different means?

Scott

On Thu, Nov 10, 2011 at 4:11 PM, Chris Howden
<chris@trickysolutions.com.au>wrote:

> To some extent your capturing sampling effort in the SD.
>
> maybe weight by the se of the mean?
>
> But overall why weight? If each CV is a single estimate why is one any
> more important than another? Once you answer that question than you
> will know how to weight.
>
> But be careful weighting, you will increase the variance of your
> estimates. So unless u need to don't.
>
> Weighting effectively reduces your sample size  To understand how much
> u can calculate the effective sample size which u can the compare to
> your actual sample size. (and which u should also use in our your
> statistical calculations that require sample size.
>
>
> What ever weighting
>
> Chris Howden
> Founding Partner
> Tricky Solutions
> Tricky Solutions 4 Tricky Problems
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> On 11/11/2011, at 4:48, Scott Chamberlain <scttchamberlain4@gmail.com>
> wrote:
>
> > All,
> >
> > I am doing a meta-analysis where our response variable is CV of effect
> > sizes (e.g., CV = sd/mean, where sd and mean are calculated from multiple
> > effect sizes from the same study).
> >
> > Does anyone know the appropriate way to weight CV?  We want to weight CV
> to
> > account for different sampling effort and uncertainty in the CV among
> > studies.  We can use standard deviation/variance of CV, but that
> > calculation includes CV in it(!!), so that larger CVs have smaller
> > variances, and vice versa (at least in our dataset).  What about
> weighting
> > CV by 1/sample size instead?
> >
> > Thanks for any guidance!
> > __
> > Scott Chamberlain
> > Rice University, EEB Dept.
> >
> >    [[alternative HTML version deleted]]
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