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st: complex multilevel analysis

From   Christian Deindl <>
Subject   st: complex multilevel analysis
Date   Wed, 17 Dec 2008 15:05:03 +0100


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,



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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