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Hi Cy.

I'll add that it might be of value to consider what it is you're trying to capture with the density predictor. Note that density is dependent on the number of nodes in the network. An alternative that may not be so heavily skewed and is not nearly as dependent on the number of nodes is average node degree. But you will have to decide whether average degree suits your research question since we have not seen anything about that.

-Nate

--
Nathan J. Doogan, Ph.D. | College of Public Health | The Ohio State Univ.



-----Original Message-----
From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Tutzauer, Frank
Subject: Re: [SOCNET] Network Density: Extremely Skewed Distribution

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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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_____________________________________________________________________
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