Julie,

1. You might want to check that structure is
affected by gender, experience, and admin position type by running an
autocorrelation test. This would tell you, for example, whether people tend to
interact more within gender than between, or certain admin position types talk
more to certain other types.

2. An n of 36 is not in itself a big problem for a
t-test. (Yes, you will need large effects to be significant with such small n,
but that is as it should be -- no scientific misconduct there, just
non-significance). There may be other reasons for distrusting a classical
t-test/anova however. E.g., your data were not obtained from a random sample,
distribution of pop variables may be different from what is required,
observations may not be independent, etc etc. Plus, you may not be interested in
whether the means are different in "the" population, which the classical test
answers among other things, but only what are the chances of obtaining a
difference in means as large as observed if centrality values are assigned
independently of gender. For all of these reasons you might
consider permutation/randomization analogs of t-test, anova,
etc.

steve.

----- Original Message -----From:[log in to unmask] href="mailto:[log in to unmask]">Julie HiteSent:Tuesday, October 15, 2002 9:03 AMSubject:Actor Attributes & Network StructureThe issue of actor attributes and network structure is the question of one of my current research papers. I am examining the structure of a network of public school administrators and, based on the graphical mapping, it looks clear that the structure is affected by gender, administrator's experience and the type of administrative position.

However, I only have n=36 administrators. At the dyadic level of the ties, I have a large enough set of cases to import network data to SPSS and use dyadic variables in correlations, etc. However, at the actor level, what types of tests can I run to determine if there are group differences? I've run t-test for gender and ANOVA for type of school (e.g. elementary, middle, high school) on actor centrality or other positional variables as well as characteristics of their egocentric networks. However, it occurs to me that the small n might a problem. If so, what small group statistics would work best for this population data to support the finding of group differences? Are there any network specific tests (in UCINet) that can address this question?

I'd appreciate any direction.

Julie

Julie M. Hite

Brigham Young University

Dept. of Educational Leadership & Foundations

Provo, UT 84602

801-422-5039

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