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  4. Missing Data Pattern Analyzer
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Examples for Missing Data Pattern Analyzer

Explore real-world examples showing how this skill works with different inputs and outputs.

Cross-Sectional Employee Survey Analysis

Analyze missing data in an employee survey with concentrated missingness in income variable (15.2%), recommending multiple imputation with m=20 and providing R/Python implementation code.

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4-Wave Longitudinal Depression Study

Analyze monotone attrition pattern in a longitudinal study with 29.5% cumulative dropout, recommending FIML growth curve modeling with lavaan and providing complete implementation code.

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RCT with Differential Dropout

Analyze differential attrition in a clinical trial (30% control vs 15% treatment dropout), recommending multiple imputation with m=40 for ITT analysis with tipping point sensitivity analysis.

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