Two-level models, also known as two-level hierarchical or multilevel models, are statistical techniques for analyzing data with a nested structure involving exactly two hierarchical levels, such as individuals within groups or repeated measures within subjects. They explicitly decompose variance into within-cluster (level-1) and between-cluster (level-2) components, typically via random intercepts and optionally random slopes. Estimation is commonly performed using maximum likelihood or restricted maximum likelihood within a linear mixed-effects framework, allowing correct standard errors, modeling of cross-level interactions, and improved inference when observations are correlated due to clustering or longitudinal dependence.
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