What is multiple imputation and why is it considered the best method for addressing missing values?

Multiple imputation is a method that imputes several (m) estimated values for each missing data point, each with a random element introduced, and then pools the results of analyses across those imputations. It is considered the best method for addressing missing values because it addresses the fundamental issue of uncertainty in any given estimate, rather than relying on a single imputed value.

Multiple imputation builds on the idea of filling in missing data with plausible estimates, but it does so multiple times. For each missing value, the researcher creates m different estimates, each incorporating a random component to reflect the uncertainty inherent in prediction. Each of the m complete data sets is then analyzed using standard statistical methods, and the results are combined or pooled to produce overall estimates and standard errors that acknowledge the extra uncertainty due to missingness. This approach is described in the chapter as currently the best method for missing value problems. It outperforms simpler methods like mean substitution or regression imputation, which underestimate variability because they treat imputed values as if they were known with certainty. Multiple imputation was previously used less often because of its complexity and limited software availability, but newer versions of statistical packages such as SPSS (version 17.0 and higher) now offer multiple imputation procedures.