Dear Eean,

This issue I have treated in the workshop during last Sunbelt. Your second approach would be more close. What you can do is restrict permutations to within teams. In UCINET you can â€Ždefine a team partition vector for each node. Further you can add team dummy variables to control for average tie strength within teams and team specific temporary effects and/or, for example treatment effects if certain teams were exposed to a specific set of conditions were others were not.

This approach could also work with a tie level analyses with ego data (to get rid of ad hoc cumulative aggregate assumptions.

Best regards,

David Dekker

Verzonden vanaf mijn BlackBerry 10-smartphone.

Van: Crawford, Eean RVerzonden: woensdag 26 augustus 2015 16:40Aan: [log in to unmask]Beantwoorden: Crawford, Eean ROnderwerp: [SOCNET] QAP on multiple independent networks with same relations |

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I believe a question like this may have surfaced on SOCNET before, but I couldn't find it in my search of the archives (either SOCNET's or my email archives). Any guidance or literature you can point me toward will be most welcome.

I have network data on 30 different teams at two points in time. The teams are quite large (average team size is 18 people, range is 16-20, total number of individual is 540), and there are no ties between teams. I want to know if ties between team members
collected during week 1 (call them X ties) predict ties between those same team members collected seven weeks later (call them Y ties).

I know how to do QAP testing for the association between ties if I were only doing it on a single team. And most of the guidance I have read on QAP testing only illustrates the procedure on a single set of network actors. My question is, what is the appropriate
way to do the same QAP testing on 30 independent teams? What do you do when you have the same network relations collected on 30 independent sets of actors? Here are two alternatives I've considered:

I conduct 30 separate QAP tests and then average the results from the tests in a sort of mini-meta-analysis.

If I do this, would I weight the individual team correlations by the network size? What would I do about the p-values (proportion of randomly permuted correlations that were as large) from the individual QAP correlations - does it even make sense to average those?QAP Team 1 X with Team 1 Y (matrices are 16 x 16)QAP Team 2 X with Team 2 Y (matrices are 18 x 18)QAP Team 3 X with Team 3 Y (matrices are 20 x 20)...QAP Team 30 X with Team 30 Y (matrices are 16 x 16)

I create the union of all 30 X matrices, the union of all 30 Y matrices. The union matrices are 540 x 540, which is the total number of individuals across all teams, and the ties within teams are arranged in blocks down the diagonal. The ties in off-diagonal
blocks (between teams) are empty represented as missing values (not zeros). I then do a single QAP test on the Union X with Union Y.

If I do this, am I biasing the QAP because its permutations include ties (between teams) that couldn't possibly have existed?QAP Union X with Union Y (matrices are 540 x 540).

Thanks for helping me think through this.

Eean

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**Eean Crawford, Ph.D.**
Assistant Professor, Dept. of Management & Organizations

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