I want to run statistical analysis of a dataset and build a logistic regression model and multinominal linear model by R according to the research question. But I was wondering which step should I use the missing value imputation to complete the dataset. I have finished the univariate analysis for each variable in the raw dataset, and I found there are three continuous variables and two categorical variables with lots of missing data. I want to use the missing data imputation to complete the dataset after processing with the bivariate analysis and graph exploration of every variable. But I am not sure if that's a correct order to do?
Should I use missing value imputation to complete the dataset before bivariate association analysis or should I do it after that?
In addition, if I want to examine the distribution of the outcome variables to find proper transformation, should I do it after impute the missing data as well?