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Subject: Re: Using node level regression analysis in Ucinet
From: Philip Leifeld <[log in to unmask]>
Reply-To:Philip Leifeld <[log in to unmask]>
Date:Fri, 9 Sep 2011 14:29:33 +0200
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*****  To join INSNA, visit http://www.insna.org  *****

Dear Kamal,

How about simply transforming your node-level data in combination with 
your permutation approach, e.g. log(data) or by using a Box-Cox power 
transformation (if all else fails)? But be careful as this might change 
the interpretation of coefficients. Please correct me if I'm wrong.

Philip


Am 07.09.2011 06:38, schrieb kamal badar:
> *****  To join INSNA, visit http://www.insna.org  *****
>
> Dear All,
>
> According to Hanneman&  Riddle (2005), predicting and testing
> hypotheses about actors non-relational attributes (e.g. their
> performance) using a mix of relational (e.g. centrality) and
> non-relational (e.g. gender) attributes, we might use node level
> regression analysis in Ucinet (Tools>Testing
> Hypotheses>Node-level>Regression). This process computes basic linear
> multiple regression statistics by OLS, and estimate standard errors
> and significance using the random permutations method for
> constructing sampling distributions of R-squared and slope
> coefficients. But how about the issue of shape of the distribution or
> normality assumption? My data meets the first assumption of using
> node level network regression but it reveals highly positively skewed
> distribution when it is tested in SPSS. Can I still use node level
> network regression? How can I defend it if some one asks about the
> skewness of my data? Is there any other kind of regression in SPSS
> which meets both assumptions?
>
> Looking forward for insightful replies in this regard.
>
> Regards
>
> Kamal Badar Doctoral Student SOM.
>
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