Should dependent variable be imputed?
Consistent with previous research, our results suggest that the dependent variable should be included in the imputation model. Under conditions where it is most practical to do so, survey users may be able to retain the imputed values in their analysis, provided that a sufficient number of datasets was generated.
Should you impute missing outcome variables?
Outcome variables must not be imputed. Predictor variables must not be imputed. Multiple imputation must not be used because you will end up with several different outcomes of your statistical analysis.
Is it better to use other variables in imputation?
Better, although still problematic, imputation approaches use other variables in the data set to predict the missing value, and contain a random component. Using other variables preserves the relationships among variables in the imputations.
How to tell if a variable is imputed in Stata?
The automatic option tells mi import ice to register all the variables and is highly recommended. It can tell which variables are regular by noting which ones never change between imputations, but it cannot distinguish between imputed and passive variables. If you have passive variables, use the passive () option and list them in the parentheses.
What should be true in multiply imputed data sets?
Certain things should always be true in multiply imputed data sets. For example, regular (unimputed) variables should have the same values in all imputations. The mi update command will check that this is so and fix any problems it finds (in this case, by setting the value in all imputations to the value in the observed data).
Why are statistics based on imputed values too small?
Since the standard errors of statistics based on imputed values, such as sample means or regression coefficients, are too small, corresponding reported p-values are also too small. P-values that are reported as smaller than they, in reality, are, lead to Type I errors.