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It's not specific to networks, but I recall a few of Andrew Gelman's old
blog posts that might be relevant to the issue: e.g.,
http://andrewgelman.com/2012/09/what-do-statistical-p-values-mean-when-the-sample-the-population/
 .

Regards,
Andy Slaughter


On Tue, Nov 20, 2012 at 7:33 AM, kamal badar <[log in to unmask]>wrote:

> ***** 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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