How can I assess the impact of missing data on my analysis results?

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How can I assess the impact of missing data on my analysis results?

Asked on Dec 19, 2025

Answer

Assessing the impact of missing data on analysis results is crucial to ensure the validity and reliability of your findings. This involves understanding the pattern and mechanism of missingness and evaluating how it might bias your results or reduce statistical power.

Example Concept: To assess the impact of missing data, first identify the missing data mechanism: MCAR (Missing Completely at Random), MAR (Missing at Random), or MNAR (Missing Not at Random). Conduct sensitivity analysis by comparing results from different imputation methods (e.g., mean imputation, multiple imputation) and complete case analysis to evaluate how missing data might alter your conclusions. Additionally, use visualization techniques like missingness maps to understand patterns and potential biases.

Additional Comment:

Use statistical tests, such as Little's MCAR test, to determine the missing data mechanism.
Consider using advanced imputation techniques like multiple imputation to handle missing data more robustly.
Visualize missing data patterns using heatmaps or bar plots to identify systematic missingness.
Evaluate the sensitivity of your results by comparing analyses with and without imputed data.
Document your findings and the potential impact of missing data on your analysis for transparency.

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