Hello,
My question is not about how Amelia works, but when should I use it in my
analysis.
I am analyzing survey data, and some variables I intend analyze are derived
from other ones. An example: I want to create, for instance, a
single variable (say, an index) of political engagement using information
from variables about different political activities -- let's suppose I want
to create an additive index of political activism summing five variables of
political activities, where 1 = the respondent took part in the activity, 0
= s/he didn't take. However, I have missing cases in these five variables I
would like to sum. Let's also suppose I don't have prior justification to
attribute either 0 or 1 to the missing cases. This way, when I add up these
five variables, I will also have some missing cases in my index.
Trying to eliminate these missing cases, how should I proceed? Should
I impute data in my original variables and, then, create my index (adding up
the already "complete" cases)? Or should I create the index using variables
with missing case and impute data later (that is, imputing data in the final
index)?
I am using an additive index as example, yet I believe this question might
also apply to other techniques as factor analysis and so one. Should missing
data be imputed before or after of this kind data processing?
Thanks,
Fabricio Fialho
-- web:
sites.google.com/site/fabriciofialho/