Dear Listers,
I am sending this again as i realised that i forgot to attach the file.
I am trying to derive the socio-economic status variable for
participants in my data set approximately 1500 participants.
I am using the used the NS-SEC method, in which the soco-economic
class of participants based on their occupation. The English Office
of National Statistics that coined the method provided the attached
vector for the derivation.
In my dataset, I derived the empstsiz (employement status) variable
based on the selfcoded occupation (occupation) variable given by
participants.
How should I proceed to derive their socio-economic class
(nssecfiveclass variable ) using the vector ? Here is what I tried,
but it doesn't look right to me.
1- I merged the data set and the vector with the command : merge
empstsiz using ns-sec_self-code_vector.dta The number of observation
in my set is 1500 as it should be, but when I compared the ocupation
variable (occupation) in my data set and the corresponding variable in
teh vector file (selfcodedoccupation) they are only identical in 300
cases. I would have expected these to be the same.
2- I also tried to join the dataset with the vector using: joinby
empstsize using ns-sec_self-code_vector.dta.
I ended up with 10 times the (15000) number of obesrvation I am
suppose to have in my original dataset. The occupation variable in my
dataset (occupation) and the occupation variable of the vector file
(selfcodedoccupation) matched in only 1441 instead of 1456 as it
should be (as I have 44 missing observations).
I am a bit confused. Can somebody please direct me on how to use such
type of vector file to obtain the nssecfiveclass variable in my data?
Thank you in advance for the help
Etan
Self-coded Occupation Empstsiz NS-SEC five class
1 1 1
1 2 1
1 3 1
1 4 1
1 5 1
1 6 1
1 7 1
2 1 1
2 2 3
2 3 3
2 4 1
2 5 1
2 6 1
2 7 2
3 1 1
3 2 3
3 3 3
3 4 1
3 5 1
3 6 1
3 7 1
4 1 1
4 2 3
4 3 3
4 4 1
4 5 1
4 6 4
4 7 4
5 1 1
5 2 3
5 3 3
5 4 1
5 5 1
5 6 4
5 7 5
6 1 1
6 2 3
6 3 3
6 4 1
6 5 1
6 6 4
6 7 5
7 1 1
7 2 3
7 3 3
7 4 1
7 5 1
7 6 1
7 7 1
8 1 1
8 2 1
8 3 1
8 4 1
8 5 1
8 6 1
8 7 1
