Esteemed Statalist users:
I am a new Stata user and have been experimenting with the hotdeck command.
I am attempting to impute values for line non-response as well as item
non-response and have come across some difficulties in setting the seed so I
can reproduce my results. The sample is stratified by reg and spec.
My first question goes as follows: is there a way to use hotdeck to impute
for both item and line non-response?
I have started out by creating a variable ("impute") that identifies all
cases for which to impute a line of data in a series of four variables
(i.e., the cases have missing values for each of the four variables.) I then
run a hotdeck command to impute:
hotdeck type size number setting, store by (reg spec) keep (id_item) imp(1)
seed (123456789)
This procedure seems to be imputing new values for each variable if any one
variable is missing (so it is imputing a new line of data for a case
requiring only item imputation). I want to be able to conserve the
information I have for those cases that are missing some (and not all) items
and only impute for the variables that are missing. To reconcile this I
attempted a second step where I ran hotdeck again on the original variables
individually for only those cases that are not complete lines of missing
data.
hotdeck type if impute==0, store by (reg spec) keep (id_item) imp(1) seed
(123456789)
hotdeck size impute==0, store by (reg spec) keep (id_item) imp(1) seed
(123456789)
etc.
I used the values generated from the first step for the line non-response
and the values from the second step for the item non-response. Upon
examination of these variables it appears like the procedure yields the same
imputed variables each time the program is run. However, when attempting to
calculate a weighted estimate of the "number" variable for the population --
I get different results each time. Is there a way to correct this?
Is there a more straight-forward way to use hotdeck imputation for item and
line non-response?
Any suggestions would be greatly appreciated.
J Wheeler
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