MULTILEVEL MODELING

Catalog description

Introduces quantitative methods for analyzing hierarchical, nested, and longitudinal data commonly encountered in the context of the behavioral, social, and health sciences. Covers mixed-effects models, multilevel modeling, variance decomposition, model comparison, missing data strategies, and longitudinal growth modeling. Emphasizes conceptual understanding, statistical reasoning, and applied data analysis using statistical software, with attention to interpretation and communication of results in research contexts.