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Following on the missing data discussion, I'd like to get feedback on an
imputation procedure that Jim Moody has helped me out with. We are
working with 26 networks of middle & highschool students, each with around
150 to 300 students. Roughly 15 percent of the students refused to
participate in the survey.
We recognized that it would be impossible to impute their missing
friendship nominations, but thought that it might be reasonable to impute
reciprocity--i.e., given that a participating A nominates a missing B, we
attempt to model whether B reciprocates.
Students were allowed to nominate up to 5 friends; among participants,
reciprocity was typically close to 50%, problematizing any wholesale
assumption about reciprocity (either treating the missing ties as 0's, or
symmetrizing the ties).
So we ran a logit on all dyads where there was at least one tie, the
dependent variable being whether the tie was reciprocated. We included
in our model various measures of tie strength (emotional closeness,
frequency of interaction, etc.), the outdegree & indegree of sender, the
indegree of the recipient, and a measure of transitivity (i.e., how many
triads would be transitive if B reciprocates).
Has anyone done something similar to this? Any thoughts?
Thanks in advance,
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