Hierarchical Linear Models


Data in the social sciences are frequently organized hierarchically---students are enrolled in classes, which exist within separate schools, which are parts of different school systems; employees work within teams within different divisions of a company; the outcomes for participants or patients in different treatment groups are measured different numbers of times and include covariates that vary over time; partners, parents, and children are parts of family units that are parts of different communities. Hierarchical data contain dependencies that preclude traditional analyses (e.g., simple ANOVA or multiple regression), requiring instead an approach that correctly estimates error sources and identifies systematic effects at their appropriate level of influence. This course provides an introduction to the analysis of hierarchical data with an emphasis on the correct identification of models, analysis of hierarchical data with current software, proper interpretation of results, and use of appropriate diagnostic tests for model adequacy. PREREQ: Psych 5066 and 5067
Course Attributes:

Section 01

Hierarchical Linear Models
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