We use multiple linear regression to perform our analyses. Because we
work with binned data (discharge frequency of a neuron) which follow a
non-normal (Poisson) distribution, we typically use the square root
transform on the dependent variable (discharge rate of the neuron).
(Actually, the transformation is sqrt(spike rate + 3/8) )
I've been trying to show that some independent variables account for
more of the variance explained in the dependent variable. However, some
researchers in my field argue that the square root transform could
artificially bias my results so that some independent variable account
for more of the variance than they really should. I don't see how this
could be from a theoretical level. Plus, I've run the multiple
regression without the transform and seen only about a 5% difference
(not much).
Does anybody know if these criticisms have any theoretical merit? I
can't see how this can be so. I thought that the square-root transform
was a pretty sound way of reducing your chance of biasing the analysis
if the data is non-normal (which most parametric tests require).
Thanks.
-Tony
--
///////////////////////////////////////////////////
// G. Anthony Reina, MD //
// The Neurosciences Institute //
// 10640 John Jay Hopkins Drive //
// San Diego, CA 92121 //
// Phone: (858) 626-2132 //
// FAX: (858) 626-2199 //
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