Good afternoon!
As mentioned in the subject, my question regards more the methodological part
that accompanies survey design and the statistical part that is involved.
So, I have the following data:
a<-data.frame (id_hh=c(1:5), strata=c(1,1,2,2,1),
Nhstrata=c(100,100,200,200,100), Nrmemb=c(2,4,2,5,4))
a$ocmemb1<-c("wk","jl","st","jl","st")
a$ocmemb2<-c("wk","jl","st","wk","wk")
where id_hh is a code of identification for the household (my analysis refers
to households), strata is the strata from which the hh is sampled, Nhstrata is
the dimension of the population strata from which the hh is sampled, nrmemb is
the no of members in a hh and ocmemb1,2...is the occupation of each individual
member of the hh (worker,jobless,student).
> a
id_hh strata Nhstrata Nrmemb ocmemb1 ocmemb2
1 1 1 100 2 wk wk
2 2 1 100 4 jl jl
3 3 2 200 2 st st
4 4 2 200 5 jl wk
5 5 1 100 4 st wk
Now, is there a possibility of designing some weights for each household based
on the characteristics of individuals which form the hh? Say, I want to
calibrate each hh for its occupational category but i don't have the additional
data for household, rather it is available for individuals, ex: I don't know
that 32% of households are included in the category of studenthh (inclusion
which is based on the status of the head of hh), but i know that 32% of all the
individuals from which the sample of hhs is drawn are all students. So, is
there a possibility of designing these weights for hhs where additional
information is available for the individuals which form that hhs? And is it a
solid way of calibrating, i mean is it reliable and trustworthy?
Thank you and have a great day!
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