Data Imputation: A Deep Dive into Analyzing the Patterns and Mechanisms Behind Missing Data

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I. Types of Missing Data
II. Identifying Missing Data
III. Patterns of Missing Data
    1. Individual Variables
    2. Bivariate Analysis
IV. Correlation Analysis
V. Missing Data Imputation
    1. Mean Imputation
    2. Median Imputation
    3. Regression Imputation
    4. Caution on Bias and MNAR Data
VI. Missing Data Mechanism
    1. Random Mechanism
    2. Time-Dependent Mechanism
    3. Mechanism Related to Another Variable
    4. Mechanism Related to the Missing Value Itself
VII. Domain Knowledge
VIII. Statistical Tests
    1. Chi-square Test for Categorical Variables
    2. t-test for Continuous Variables
    3. ANOVA (Analysis of Variance)
    4. Kruskal-Wallis Test
    5. Logistic Regression
    6. Correlation Analysis
    7. Propensity Score Matching
IX. Data Imputation Evaluation
    1. Visual Comparison
    2. Summary Statistics
    3. Correlation Analysis
    4. Model Performance
    5. Sensitivity Analysis
    6. Cross-Validation

Analyzing missing data is a crucial step in the data preprocessing phase, as missing values can significantly impact the quality and reliability of your analysis. Understanding the patterns and mechanisms behind missing data helps you make informed decisions on how to handle them appropriately. Here’s a deep dive into analyzing missing data: