Recognizing clustered and longitudinal data structures, this chapter introduces linear mixed models (LMMs). We review random effect specification, restricted maximum likelihood (REML) estimation, and inference under unbalanced designs. Special attention is paid to split-plot experiments, repeated-measure designs, and cross-classified data common in education and panel studies. We outline model selection heuristics and present information-criteria benchmarks. Practical guidance on software and diagnostics (BLUPs, conditional residuals) rounds out the chapter.