Cieľová skupina
Táto časť nie je lokalizovaná
Researchers in psychology, education, social science, medicine, and business, or others analyzing data with multilevel nesting structureŠtruktúra kurzu
Táto časť nie je lokalizovaná
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.