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Subject:

Analysis of actor-level variables

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Wed, 21 Jan 2004 18:09:45 EST

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 ```***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** In the context of an public school administrative network, I am trying to analyze the relationship between actor-level characteristics and 1) egocentric network structure and 2) centrality. N=34 adminstrators. IV: Characteristics     PERSONAL (gender, age),     ADMINISTRATIVE POSITION (administrative level, school level), and           --PROXY for power/authority     ADMINISTRATIVE EXPERIENCE (years in admin, number of schools as administrator)           --PROXY for time & familiarity across network DV: Network Structure      EGOCENTRIC NETWORK STRUCTURE (size, density, % bidirectional ties, mean strength of ties)      NETWORK CENTRALITY (betweenness, closeness & eigenvector)                                              (not degree as this is bascially being used for size) Previous analysis of this network has been done at the dyadic level (max n=1122). However at the actor level, the n=34. Assuming a case study approach where the entire population is included (no random sampling), what is the best way to demonstrate relationships between variables?     Creating categories, I have done this using t-test and ANOVA.     I have also used correlation for the interval data. However, it seems like it would be much beneficial (and succinct) to present regression models. My question is how will a regression analysis with N=34 (non-random / whole population) be critiqued and what is the best way to present it to respond to these potential critiques (e.g. small n). Clearly the findings would only be descriptive of this network and not generalizable to other networks. Would presenting it as a descriptive analysis solve the small n? I would appreciate any suggestions for how to approach and present this analysis. Julie Julie Hite Brigham Young University [log in to unmask] _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.sfu.ca/~insna/). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.```