***** 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 sampleof 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. _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.