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Researchers in psychology, education, social science, medicine, and business, or others analyzing data with multilevel nesting structureStruktura kurzu
Tato část není lokalizována
Introduction to Multilevel Models
- Nested data structures.
- Ignoring dependence.
- Methods for modeling dependent data structures.
- The random-effects ANOVA model.
Basic Multilevel Models
- Random-effects regression.
- Centering predictors in multilevel models.
- Model building.
- A comment on notation (self-study).
- Intercepts as outcomes.
Slopes as Outcomes and Model Evaluation
- Slopes as outcomes.
- Model assumptions.
- Model assessment and diagnostics.
- Maximum likelihood estimation.
The Analysis of Repeated Measures
- The conceptualization of a growth curve.
- The multilevel growth model.
- Time-invariant predictors of growth (self-study).
- Multiple groups models.
Three-Level and Cross-Classified Models
- Three-level models.
- Three-level models with random slopes.
- Cross-classified models.
Multilevel Models for Discrete Dependent Variables
- Discrete dependent variables.
- Generalized linear models.
- Multilevel generalized linear models.
- Additional considerations.
Generalized Multilevel Linear Models for Longitudinal Data (Self-Study)
- Complexities of longitudinal data structures.
- The unconditional growth model for discrete dependent variables.
- Conditional growth models for discrete dependent variables.
