What are the advantages of multiple imputation in data analysis?

What are the advantages of multiple imputation in data analysis?

Multiple imputation helps to provide more accurate estimates by filling in missing data multiple times, allowing for variability.

How does it compare to other methods for handling missing data?

Unlike single imputation, which can underestimate variability, multiple imputation accounts for uncertainty by creating several datasets.

Can you explain how the process works?

Sure! It involves creating multiple complete datasets by imputing missing values, analyzing each dataset, and then pooling the results.

What are some common applications of multiple imputation?

It's commonly used in fields like healthcare, social sciences, and any research involving survey data where missing values are prevalent.

Are there any limitations I should be aware of?

Yes, it assumes that data is missing at random, and the quality of imputation depends on the model used.