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all:

I am new to social network analysis, please pardon if this is a simple question.

I am using GEPHI to create a network.

My data is a set of ratings by peers in group settings (sociometric). All peers rated all other peers in the group on a scale from 1-4 that tapped into "liking". Some youth have ADHD, some do not. I would like to use SNA to determine whether kids with ADHD are on the outskirts of the network (less connections) based on less liked.

It seems that the rankings are a weight, but the SNA that I am getting is simply that all kids in a group are connected -- because they all rated each other.

What am I missing here? How can I formulate this model to answer my question better?

Thanks! Andrea

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A simple binary network can be constructed as follows: ratings: 1 and 2 treated as 0; ratings: 3 and 4 treated as 1

Now you have a directed binary network, which you can model with an Exponential Random Graph Model (ERGM), in which you can specify many terms of interest, such as

  1. number of edges: this captures the overall density of the network.

  2. number of mutual edges: this captures the tendency of forming mutual edges.

  3. nodematch: this captures tendencies of forming edges within group ADHD and group non-ADHD.

  4. transitivity (GWESP term): captures the tendency of forming edges because they have common connections.

http://www.statnet.org/ provides the statnet R package

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  • $\begingroup$ thank you. this is helpful. can you point me in the direction (if it exists) for a statistical test to compare node strength or other statistics across groups - e.g.,to answer if ADHD kids have singificantly less centrality or node stregth than non-adhd? $\endgroup$ – user1638567 Dec 14 '15 at 16:25
  • $\begingroup$ @user3722874 How many kids do you have? I guess not many? I think some t-test can be helpful, for example, compare average rating within the ADHD group and the average rating within the non-ADHD group; compare the average rating of ADHD group towards the non-ADHD group and the average rating of non-ADHD group towards the ADHD group $\endgroup$ – mac Dec 15 '15 at 1:23
  • $\begingroup$ @user3722874 In the directed binary network context, if by centrality you mean "popularity" of each node, then in ERGM, you can use terms: "receiver" and "sender", which captures the number of connections each node received and sent $\endgroup$ – mac Dec 15 '15 at 1:41

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