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Just thought of something. Since you are dealing with density, your values are probably not all EQUAL to zero, but maybe a variety of values near zero. Then you might be able to find a suitable transformation. Good luck!

Sent from my iPhone - Frank

> On Jun 9, 2017, at 9:57 AM, Chao yawo <[log in to unmask]> wrote:
> 
> ***** To join INSNA, visit http://www.insna.org *****
> Hello,
> 
> Good morning.  I am writing to ask for some advice on possible transformation strategies for a 
> network density variable that serves a a predictor (IV) in a logistic regression model.
> 
> The distribution is extremely skewed (with a lot of zeros) - in fact the mean approaches 0.  but I believe the zeros are meaningful, reflecting extremely sparse network, and their effect need to be captured in the model. 
> 
> What are some of the ways networkers deal with skewed distributions.,  I can categorize the variable (I am hesitant to do that, for well known cautions against categorizing interval level measures).  A second option is to transform it (e.g., log it).   But I am wondering if there is any other way to deal with this issue or the advisability of the two options I am considering.
> 
> thanks in advance,
> 
> Cy
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