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On Tue, 20 Nov 2012, Garry Robins wrote:

> ***** To join INSNA, visit http://www.insna.org *****
> 
> Using standard General Linear Model tests with network data is inferentially risky because the most
> fundamental assumption – independence of observations – is explicitly undermined by the use of a
> network perspective. If observations are independent, then there is no network;

A friendly amendment -- some dyad-independent processes, like homophily on 
exogenous attributes, operate on networks.

> if observations are
> dependent, standard inferential tests are not valid. So, in that sense, a standard statistical test
> used with network data is a contradiction.  There are plenty of network statistical methods and models
> that can be applied.
>
> 
> 
> Professor Garry Robins
> 
> Melbourne School of Psychological Sciences
> 
> University of Melbourne
> 
> http://www.psych.unimelb.edu.au/people/garry-robins
>
> 
> 
> Melnet website: http://www.sna.unimelb.edu.au/
>
> 
> 
> Check out our new book on ERGMs: Lusher, D., Koskinen, J., & Robins, G. (2012). Exponential random
> graph models for social networks: Theory, methods and applications. Cambridge University Press.
> (http://www.cambridge.org/us/knowledge/isbn/item6897868/?site_locale=en_US)
> 
> Look inside the book:
> (http://www.amazon.co.uk/Exponential-Random-Models-Social-Networks/dp/0521141389#)
>
> 
> 
> From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of kamal badar
> Sent: Tuesday, 20 November 2012 11:33 PM
> To: [log in to unmask]
> Subject: Implications/limitations of applying inferential statistics to co-authorship network data
>
> 
> 
> ***** To join INSNA, visit http://www.insna.org *****
> 
> Dear All,
>
> 
> 
> According to Hanneman & Riddle (2005) " Social network analysts rarely use samples in their work. Most
> commonly, network analysts identify a population and conduct a census of that population. The
> boundaries are those imposed by the researcher or even created by the actors themselves. Social network
> studies, therefore often draw the boundaries around a
> 
> population that is known, a priori, to be a network" (Page 5). 
>
> 
> 
> Talking about co-authorship networks, we collect bibliometric data from databases according
> to boundaries imposed (geographic location of researchers, disciplinary fields, journals within the
> fields or individual institutions or departments ect). If the co-authorship network understudy is
> considered a population, what implications/limitations can we have while
> applying inferential statistics (e.g. correlation and OLS regression) to a specific phenomenon (for
> e.g. examining the association of centrality and academic performance)? Doesn't the exercise of
> inferential statistics provide types of estimates of population parameters and characteristics based on
> a sample of that population not the population itself? How can we defend if we do indeed
> apply inferential statistics to co-authorship network? 
>
> 
> 
> Hoping to get important insights from the experts.
>
> 
> 
> Regards
> 
> Kamal Badar
> 
> Doctoral Student
> 
> Asian Insitute of Technology
> 
> Thailand. 
> 
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