Dr. King,
I'm sorry to keep pestering you with these questions; unfortunately, there is no
one in my department with the kind of mathmatical training necessary to
completely decipher the EMis algorithm ;-). I think I understand what you
proscribe in King et al (2001) but I'd like to confirm it.
(please note, I have not tried any of the following - I'm using an old PII with
128mb of RAM. Every attempt to run Amelia is a major chore - having to shut
down all other background routines, etc. So, this time I'm asking first ...)
Presume I have variable that counts the number of mentions (in an open-ended
context) of a particular subject (in this case, crime as the "biggest
problem"). This seems to me to be most closely related to a event-count type
variable, implying that I should take the square root (or some partial power) to
stablize the variance, etc.
However, conceptually this variable is measuring salience which must be bounded
at zero, implying I should be applying some form of non-linear (logistic,
perhaps) transformation.
Empirically, because of the significant positive skew of the variable, Amelia
returns a large number of negative imputed values. What can be done about this
situation (aside from springing for Gauss - which my department won't do)?
I can see three options:
1. impute from the raw data, either leaving the continuous values or truncating
any negative imputations to zero.
2. impute from transformed data using one or the other transformation
3. impute from the transformed data using both transformations
Which seems best to you?
If I do apply a transformation before imputation, do I "un-transform" the data
after (in the case of the square-root, I'd say no because any negative
imputations produced by Amelia would have their signs reversed...)
Again, thank you for your time
--
Matthew "ElectricBlooz" Vile
UNO Survey Research Center
http://www.swd.org/mardi/blooz.htm
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