Missing Data Strategy Comparator

Compare different methods for handling missing data and their impact on analysis

Data Generation

5% - 50%

Imputation Methods

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How to Use
  1. Set the sample size for your simulated dataset
  2. Choose a missingness pattern (MCAR, MAR, or MNAR)
  3. Set the percentage of data to be missing
  4. Select which imputation methods to compare
  5. Click "Generate & Compare" to see results
  6. Review the statistical comparison and bias analysis
Missingness Patterns
  • MCAR: Missing values are completely random, unrelated to any variables
  • MAR: Missingness depends on observed variables but not the missing value itself
  • MNAR: Missingness depends on the unobserved value itself
Imputation Methods
  • Listwise Deletion: Remove all cases with any missing values
  • Mean Imputation: Replace missing values with variable mean
  • Median Imputation: Replace missing values with variable median
  • Regression Imputation: Predict missing values using other variables
Understanding Bias
  • Bias in mean: Difference from true population mean
  • Bias in variance: Underestimation or overestimation of variability
  • Bias in correlation: Artificial strengthening or weakening of relationships
  • Green indicators show low bias, yellow moderate, red high bias