What are the best practices for multiple imputation?

What are the best practices for multiple imputation?

The best practices for multiple imputation include ensuring that the imputation model is appropriate, using a sufficient number of imputations, and checking the convergence of the imputation process.

How do I choose the right imputation model?

Choosing the right imputation model depends on the nature of your data. Consider the distribution of your variables and whether they are continuous or categorical.

What is the recommended number of imputations?

A common recommendation is to use at least 5 to 10 imputations, but more may be needed depending on the amount of missing data.

How can I assess the quality of my imputations?

You can assess the quality of your imputations by comparing the distributions of the imputed data to the observed data and checking for consistency across imputations.

Are there any software tools you recommend for multiple imputation?

Yes, popular software tools for multiple imputation include R packages like 'mice' and 'missForest', as well as Python libraries like 'fancyimpute'.