What are the limitations of multiple imputation in statistical analysis?

What are the limitations of multiple imputation in statistical analysis?

Multiple imputation can introduce bias if the imputation model is incorrect, and it may not handle non-ignorable missing data well.

Can you explain how the choice of imputation model affects the results?

Yes, the choice of model can significantly impact the estimates. If the model doesn't reflect the true data structure, it can lead to misleading conclusions.

Are there specific scenarios where multiple imputation is not recommended?

Yes, it's not recommended when data is missing not at random (MNAR) or when the missing data mechanism is complex and unknown.

What alternatives to multiple imputation exist?

Alternatives include complete case analysis, maximum likelihood estimation, and Bayesian methods, each with its own pros and cons.

How can I assess the quality of my imputations?

You can assess quality by comparing the distributions of imputed and observed data, checking for consistency, and conducting sensitivity analyses.