I am in the data preparation stages of conducting a multiple regression analysis of US health survey data. The first task I have decided to do is impute missing values from the dataset of 8 variables (1 dependent 7 independent).
I have exactly 6000 entries for each variable in the dataset and am trying to impute for the missing values in variable x (continuous numerical variable) and y (categorical factor variable).
x = 828 missing values
y = 239 missing values
I can't seem to understand what imputation method I should use. I understand that MI is better for when there is greater than 10% missingness but only one of the two variables above is missing by greater than 10% of its total entries...
Is it that I am supposed to use a mixture of both? (MI for x and HD for y)
Any help would be much appreciated.
The five remaining variables consist of:
One Dependent variable
One Continuous numerical Independent Variable
Three Categorical Factor Independent Variables