I have a nested dataset and I need advice on what types of analyses
would be appropriate to use with it. The data are collected from groups
of siblings; in each family, each sibling answers questions regarding
each of the other siblings. So in each family:
Obs 1 Sibling 1 rates sibling 2
Obs 2 Sibling 1 rates sibling 3
Obs 3 Sibling 2 rates sibling 1
Obs 4 Sibling 2 rates sibling 3
Obs 5 Sibling 3 rates sibling 1
Obs 6 Sibling 3 rates sibling 2
Obviously, these observations are not independent. First of all, there
are family groups, but that's easy to deal with. But what do I do about
the fact that siblings are rating each other? I could make the family
to be the third level unit, individual siblings -- the second level, and
their ratings of specific "target" siblings -- the first level (i.e.
group together obs 1 and 2; obs 3 and 4; obs 5 and 6). But the lack of
independence remains with such grouping, because target siblings are the
same -- i.e. we could also group obs 1 and 6; obs 2 and 4; and obs 3 and
5. So each observation is cross-nested, but not in a typical sense of
belonging to two different types of groups on the same level (e.g.,
schools and neighborhoods, or boys and girls, etc.) -- they just belong
to two "groups" of the same type at once. If anyone has any advice, I'd
appreciate it.
