Description
Multilevel Models for Hierarchical Data focuses on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of hierarchical data. Hierarchical data structures arises from sampling units at multiple levels, wherein lower-level units are nested within higher-level units. Common instances are students nested within schools, siblings nested within families, patients nested within therapists, and adults nested within communities. Analyzing such data using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so. Not only are tests of significance likely biased, but many important within-group and between-group relations cannot be evaluated. All of these limitations can be addressed within the multilevel model. In this workshop we provide a comprehensive exploration of multilevel models for hierarchical data with topics ranging from introductory to advanced.
