Is mean or mode better for replacing missing data for an ordinal scale? I'm thinking mode is better because the respondent has to choose between integer values (1, 2 and so on) bu I am wondering is mode imputation doesn't create bias by favoring the value that appears most often. Thank you!
Solved – Mean versus imputation for missing data in the case of an ordinal scale
missing data
Best Answer
The best choice is to use an R package like mi (mice, or amelia should also work). It will scan your data and propose the appropriate format for each variable (which you can also manually change if needed)
In the case of categorical (ordered or not) variables in the MI package - a series of chained, ordinal regressions are run with the variables targeted for imputation as the DVs. This is done until some convergence criteria are met (which can be adjusted in the package
You could think of it as a markov chain of regressions using all included data to predict missing data in all of the variables you included in the analysis