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, Lund University Box 52, SE-221 00 Lund, SWEDEN Ph: +46 46 222 8093 Email: jan.teorell(a)svet.lu.se