Hi Amelia-folks, I have a few questions regarding
variable transformations in the imputation models:
1. Do you I need to use the same variable
transformations in my analysis model as in the imputation model? If, for
example, I use the quadratic root transformation to normalize a couple of event
count variables, do I have to use the same root transformed variables in my
analysis model or could I use the “raw” variables?
2. I have a couple of variables (e.g., oil exports as
percentage of GDP) that are highly positively skewed, containing many
zeros but with a long tail. What kind of transformation would be advisable in
that case (I gather the natural log might not be a good idea since there are so
many zeros; moreover, the results look weird if I log these variables)?
3. I have a variable (a democracy index) which ranges
from 0-10 and is pretty severely bimodal, that is, with most of the cases falling
at either low or high values. What kind of transformation could help normalize
this variable?
4. Relatedly, my dependent variable is the annual
change (first difference) of this democracy index. The imputation model does an
extremely poor job at predicting this variable (judging from the overimputation
plot), however, which makes substantively sense in light of my analytical
results. Would a better idea then be to impute the level and lagged level of
this variable, then compute the annual change variable from these imputed variables,
and run the analysis?
All the best,
Jan Teorell
Docent, Associate Professor of Political Science
Department of Political Science,
Box 52, SE-221 00
Ph: +46 46 222 8093 Email: