PSYCHOLOGY 205D Section: 1
Spring Quarter 2007
Hierarchical data arise from a variety of situations: studies of clustered data (e.g., students nested within classrooms, patients nested within clinics), repeated measures where observations are nested within individuals (e.g., learning, reading), longitudinal data (repeated observations over time), and spatially correlated data (e.g., individuals nested within geographic settings). This course concerns multilevel analysis of normal, hierarchically structured data, including cross-sectional clustered data, repeated measures, and longitudinal data. Broadly, topics include hierarchical linear and nonlinear models and latent curve models. Will will also consider how these methods may be used to handle unbalanced and missing data.
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|Classroom||Class Schedule||Course Website|
|157 Young||W 9:00 AM - 1:00 PM|
|Instructor||Instructor Email||Office||Office Hours|
|Shelley Blozis , Ph.D.||174F Young Hall||Wed 2pm-5pm|