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Statistics
Categorical Data Analysis
1. Foundations of Categorical Data
2. Descriptive Analysis of Categorical Data
3. Probability Foundations for Categorical Data
4. Single Variable Inference
5. Two-Variable Analysis
6. Stratified Analysis
7. Logistic Regression
8. Multinomial Response Models
9. Loglinear Models
10. Advanced Topics
11. Computational Methods
6.
Stratified Analysis
6.1.
Three-Way Tables
6.1.1.
Partial Tables Construction
6.1.2.
Conditional Relationships
6.1.3.
Marginal vs Conditional Associations
6.2.
Confounding and Effect Modification
6.2.1.
Simpson's Paradox
6.2.1.1.
Definition and Examples
6.2.1.2.
Mechanisms and Explanations
6.2.1.3.
Detection Methods
6.2.2.
Confounding Assessment
6.2.2.1.
Crude vs Adjusted Measures
6.2.2.2.
Confounding Criteria
6.2.3.
Effect Modification
6.2.3.1.
Interaction Concepts
6.2.3.2.
Homogeneity Testing
6.3.
Mantel-Haenszel Methods
6.3.1.
Common Odds Ratio Estimation
6.3.1.1.
Weighted Average Approach
6.3.1.2.
Confidence Intervals
6.3.2.
Mantel-Haenszel Test
6.3.2.1.
Test for Common Odds Ratio
6.3.2.2.
Stratified Analysis Applications
6.3.3.
Breslow-Day Test
6.3.3.1.
Homogeneity of Odds Ratios
6.3.3.2.
Test Statistic and Interpretation
6.4.
Standardization Methods
6.4.1.
Direct Standardization
6.4.2.
Indirect Standardization
6.4.3.
Age-Adjusted Rates
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5. Two-Variable Analysis
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7. Logistic Regression