Multiple Imputation
- Generates several complete datasets by imputing different plausible values for missing entries.
- Analyzes each completed dataset and uses the variation across them to reflect uncertainty from missing data.
- Helps produce more accurate and reliable estimates than relying on a single imputation.
Definition
Section titled “Definition”Multiple imputation is a statistical technique used to account for missing data in a dataset. The method involves generating multiple versions of the dataset, each with different values for the missing data, and then using these different versions to estimate the effects of the missing data on the analysis.
Explanation
Section titled “Explanation”Multiple imputation creates several alternate, completed datasets by filling in missing values with different plausible values. Each completed dataset is analyzed as if it were fully observed, and the set of results is used to assess the impact of missing data and to reflect the uncertainty introduced by those missing values. This approach allows researchers to incorporate the variability due to imputation into their final inferences.
Examples
Section titled “Examples”Income and health outcomes
Section titled “Income and health outcomes”In a study examining the relationship between income and health outcomes, if some participants do not report their income, multiple imputation can generate multiple versions of the dataset, each with different imputed values for the missing income. These different versions can then be used to estimate the relationship between income and health outcomes, accounting for the uncertainty due to the missing data.
Educational attainment and employment outcomes
Section titled “Educational attainment and employment outcomes”In a study examining the relationship between educational attainment and employment outcomes, if some participants do not report their educational attainment, multiple imputation can generate multiple versions of the dataset, each with different imputed values for the missing educational attainment. These different versions can then be used to estimate the relationship between education and employment outcomes, accounting for the uncertainty due to the missing data.