hi,
I have a question regarding the analysis of triads using multilevel-models.
I'm conducting an international comparison of financial transfers from
parents to their children.
each respondent can have up to four children, and each child is an
observation, with children nested within respondents nested within
households nested within countries.
So I have four levels.
Since transfers are not only affected by the characteristics of children
but also by parents I 'm trying to analyse triads.
the datastructure is as follows:
Triad Dyad(Parent) Dyad(Child) respondent
1 mother1 child 1 1
2 father1 child 1 1
3 mother1 child 2 1
4 father1 child 2 1
1 mother2 child 1 2
2 father2 child 1 2
3 mother2 child 2 2
4 father2 child 2 2
. . .
. . .
two problems in regard to mulitlevel-analysis arise with this structure.
1) each child is doubled for each parent and for each respondent
2) parents are doubled with children, but unique for each respondent
as far as I know this is a case of cross-classification.
to deal with the nonindependence of children and parents I build a dummy
variable for children (1 for the first appearance, zero for any further
appearance) and for parents.
this dummy-variables are included in the modell as random slopes for the
respondents (see syntax below).
since I couldn't find a clear expamle in the literature I'm not quite
sure if I'm correct.
Can anyone give me some advice?
best regards,
christian
*Syntax:
gen dumkind=1
replace dumkind=0 if dyadKINDER==dyadKINDER[_n-1] & persid==persid[_n-1]
gen dumeltern=1
replace dumeltern=0 if dyad_e==dyad_e[_n-2] & persid==persid[_n-2]
xtmelogit transfer_k /*
*/ || land: || hhid: || persid: dumkind dumeltern, or
*
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