Imputation has become one of the most popular tools used to deal with missing value problems in survey data analyses. After providing some background on important issues related to imputed data, a description of the results of a comprehensive assessment of 11 of the most popular methods is provided. Results indicate "best" performing methods when the population is normally distributed and indicate that the performance of most of these methods deteriorates as the population deviates from the normal distribution. A least trimmed squares (LTS) regression approach is shown to be robust with outlier or skewed data.