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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
9.
Loglinear Models
9.1.
Model Framework
9.1.1.
Poisson Regression Connection
9.1.2.
Log-Linear Formulation
9.1.3.
Cell Count Modeling
9.2.
Model Types
9.2.1.
Independence Models
9.2.1.1.
Complete Independence
9.2.1.2.
Mutual Independence
9.2.1.3.
Conditional Independence
9.2.2.
Association Models
9.2.2.1.
Two-Factor Interactions
9.2.2.2.
Three-Factor Interactions
9.2.2.3.
Higher-Order Interactions
9.2.3.
Saturated Models
9.2.3.1.
Perfect Fit Concept
9.2.3.2.
Parameter Interpretation
9.3.
Model Fitting and Selection
9.3.1.
Maximum Likelihood Estimation
9.3.2.
Iterative Proportional Fitting
9.3.3.
Model Comparison Methods
9.3.4.
Hierarchical Model Selection
9.3.5.
Backward and Forward Selection
9.4.
Parameter Interpretation
9.4.1.
Main Effects
9.4.2.
Interaction Effects
9.4.3.
Lambda Parameters
9.5.
Goodness-of-Fit Assessment
9.5.1.
Deviance Statistics
9.5.2.
Pearson Chi-Square
9.5.3.
Standardized Residuals
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10. Advanced Topics