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Agreed Martina.  I hadn't seen your post before I pressed send.


From: martina morris [mailto:[log in to unmask]]
Sent: Friday, January 17, 2014 4:00 PM
To: Frank, Kenneth
Cc: Social Networks Discussion Forum
Subject: Re: Clustering of networks / comparison across organisations


That's of course, what the loglinear model will estimate, just with a
bit more information and flexibility wrt which odds-ratio(s) are
estimated.

On Fri, 17 Jan 2014, Frank, Kenneth wrote:

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>
> I am a big fan of the odds ratio: AD/BC from table below.  Or the log-odds.  This
> links to how you would model the effect of team membership on a tie.  Odds ratios are
> insensitive to marginals.
>
>
>
>                                                 Tie
>
>                                   No                        yes
>
>                                 ------------------------
>
> Same team  No |    A                       B
>
>                         yes |    C                       D
>
> see Goodman, L. and W. Kruskal 1954 "Measures of association for cross
> classifications." Journal of the American Statistical Association 49: 732-764.
>
>
>
>
>
>
>
> Ken Frank
>
> Professor
>
> Measurement and Quantitative Methods
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> Counseling, Educational Psychology and Special Education
>
> And
>
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>
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> From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of
> Kerstin Sailer
> Sent: Friday, January 17, 2014 7:04 AM
> To: [log in to unmask]<mailto:[log in to unmask]>
> Subject: Clustering of networks / comparison across organisations
>
>
>
> ***** To join INSNA, visit http://www.insna.org *****
>
> Dear All,
>
> I would like to do a comparison of different organisations and their network
> structures (nodes are people, ties are frequency and usefulness of contacts, sizes
> vary significantly from n=100 to n=1000; data is survey-generated; key question was to
> identify the top 25 contacts from a list of everyone in the organisation and then give
> details on these contacts).
>
> One of the metrics I would like to compare (and where comparison is not
> straightforward at all, hence my email to ask for help / advice) is the E-I index,
> i.e. the degree to which contacts are within teams or across teams.
>
> The difficulty is that team sizes and numbers of teams within an organisation differ
> so much. For instance if organisation A has 10 teams of 10 members each, every
> participant would have to nominate members from outside their team to come up with 25
> top contacts, hence the degree of external contact might be higher by default than for
> an organisation B with 2 teams of 50 members each, where each participant could
> possibly nominate all 25 top contacts within their own team.
> This is further complicated by the fact that not everyone participated in the survey
> (i.e. missing ties), that not everyone nominated 25 people (most people don't count
> and just use this as a rough guideline, or insist on nominating fewer or more), so
> outdegree is not always 25 for each member and of course this could vary by team as
> well (so members of one team, e.g. HR might nominate more people disproportionately if
> compared to the organisation's average because of their outreach role).
>
> Now, if anyone has come across any discussion of those problems in the literature, or
> anyone mathematically minded on the list has an idea on how to normalise these metrics
> so that they become comparable, I'd be very happy to hear about it!
>
> Thanks in advance!
> Best,
> Kerstin
>
>  --
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>  Dr Kerstin Sailer
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