***** To join INSNA, visit http://www.sfu.ca/~insna/ ***** I am working on a research project that has the following (hypothetical) design. Say that we have 10 individuals in some community and we want to predict whether a friendship exists between each pair of individuals. A matrix of non-directional friendship links (1 = yes, 0 = no) can be formed with 45 elements (i.e., a lower-diagonal matrix with no self ties). I'm a relative newcomer to quantitative network studies, but the seemingly simple analytic design I came up with was to create a data set with 45 lines of data (one for each potential pairing). The dependent variable on each line would be the aforementioned 1 or 0 for existence of a friendship. Each line would also contain several predictor variables for the potential pair, some dichotomous (e.g., do they work for the same firm?, again scored 1 or 0) and some quantitative (e.g., how many blocks apart do they live?). One could then perform a logistic regression with the dichotomous DV and the various predictor variables. An odds ratio associated with each predictor would reveal whether the predictor appeared to contribute to pairs' being friends with each other. I just finished reading Wasserman and Faust's "Social Network Analysis" (in toto) and I did not find any analytic strategy like the one I described above (as best I could tell). Wasserman and Faust focused more on blockmodels, popularity and reciprocity parameters, etc. I do recognize that the design I've proposed has a potential problem with non-independence of observations (i.e., the same individual is implicated in several potential pairings). My question is twofold: (a) putting aside the independence issue for the moment, does my design sound reasonable? and (b) could the independence problem be overcome (at least to some extent) by using alpha levels more stringent than the usual .05 in order to adjust for the (presumably) inflated degrees of freedom in my design? Thanks, Alan Reifman, Ph. D., Associate Professor Dept of Human Dev't and Family Studies College of Human Sciences Texas Tech University Lubbock, TX 79409-1162 (806) 742-3000 http://www.hs.ttu.edu/hdfs/Faculty/reifman.htm _____________________________________________________________________ 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.