Assess the ability to apply two- and three-level multilevel (hierarchical) models to partition variance in student achievement, specify and interpret fixed and random effects for teacher- and school-level predictors, choose centering strategies, test cross-level interactions, compute and interpret ICCs and variance components, compare models (ML/REML, likelihood/BIC), perform diagnostics, address complex sampling/weights and missing data, interpret common software output (e.g., lme4, Stata), and translate results into policy-relevant conclusions for educators and researchers.