Mixed Models for Continuous Data

Summary

Develops the linear mixed-effects model, also known as the multilevel or hierarchical linear model, building from a random intercept that lets each cluster have its own baseline, with variance components partitioned into between-cluster and within-cluster pieces and the intraclass correlation coefficient falling out directly. Extends to random slopes that let predictor effects vary across clusters with a bivariate Normal distribution and intercept-slope covariance, then covers REML estimation, BLUPs and shrinkage, fixed-versus-random-effect specification choices, and contextual effects where group means carry information distinct from individual deviations.