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Researchers often impute continuous variables under an assumption ofnormality, yet many incomplete variables are skewed. We find that imputingskewed continuous variables under a normal model can lead to bias; the bias isusually mild for popular estimands such as means, standard deviations, andlinear regression coefficients, but the bias can be severe for moreshape-dependent estimands such as percentiles or the coefficient of skewness.We test several methods for adapting a normal imputation model to accommodateskewness, including methods that transform, truncate, or censor (round)normally imputed values, as well as methods that impute values from a quadraticor truncated regression. None of these modifications reliably reduces thebiases of the normal model, and some modifications can make the biases muchworse. We conclude that, if one has to impute a skewed variable under a normalmodel, it is usually safest to do so without modifications -- unless you aremore interested in estimating percentiles and shape that in estimated means,variance, and regressions. In the conclusion, we briefly discuss promisingdevelopments in the area of continuous imputation models that do not assumenormality.

Should a Normal Imputation Model Be Modified to Impute Skewed Variables?