Handling missing data is a critical aspect of ensuring data quality. Strategies to address missing data include:
Imputation: Use statistical methods to estimate and replace missing values. Sensitivity Analysis: Assess how the results might change under different assumptions about the missing data. Complete Case Analysis: Analyze only the cases with complete data, though this can reduce sample size and statistical power.
