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Dear Socnetters,

This is a question about model choice with categorical independent
variables.

I have categorical attributes, and network data.

When I look at the attributes one-by-one, and produce contingency tables
with the dyadic data, each attribute has a highly significant chi-squared.

When I look at them with QAP multiple regressions approach, the R-squared is
practically zero. The network is sparse with about a thousand nodes.

Of course, a QAP regression with a binary dependent variables is not really
appropriate. I think when coefficients are referred to as probabilities in
the QAP linear regression context, it is not appropriate. Like they are
referred to here:

http://faculty.ucr.edu/~hanneman/nettext/C18_Statistics.html#tworeg

But even when I calculate geodesics (and have an interval scale dependent
variable with an approximately normal distribution) I have no R-squared.

So:

Is there a way to model multiple, node level categorical independents, and a
dyadic dependent variable?

Log-linear models?

A logistic regression model?

Thanks
Balazs

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