The era codified multilevel modeling as a robust framework for analyzing hierarchical data, enabling precise partitioning of within- and between-subject variance and the analysis of growth trajectories over time. It catalyzed broad adoption across psychology, education, and the social sciences, by integrating hierarchical linear models with latent-variable approaches for complex dependency and non-normal outcomes. Foundational work unified growth modeling, cross-level covariance estimation, and latent multilevel modeling, establishing a durable standard for longitudinal and nested data analysis.