Pennsylvania State University 3 credits HDFS
This course provides an overview of multilevel regression models for students pursuing applied statistical analysis of correlated data. We will consider the primary applications for multilevel models that involve working with data that have hierarchical/nested groupings, repeated measures, and extensions of both (e.g., more involved growth trajectories). This course will cover how best to approach manipulating both fixed and random effects (i.e., in a "mixed model") as well as assessment of model fit. We will work with the type of data that are suitable for multilevel analysis, where estimated parameters can represent both broader and more specific contextual levels. Presentation of theory will cover across-discipline uses of these models (and the various other names they go by such as hierarchical linear models, random coefficient models, etc.) and will aim to set a foundation for those who want to pursue more advanced modeling approaches (e.g., using latent constructs) in future coursework.
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