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Feb 16, 2025
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STAT 8450:Multilevel Statistical Modeling 3 Credit Hours Prerequisite: Admission to a KSU PhD Program Data often have a structure that is nested or hierarchical. For example, when
investigating student outcomes, we need to consider that students are nested inside
classrooms that are in turn nested inside schools. Clustered data violate the
assumption of independence of error terms expected of the general linear model
family, which includes ANOVA and regression. Multilevel models allow us to analyze
data where observations are nested and not independent, correctly modeling correlated errors and avoiding biased standard errors and parameter estimates. This course will focus on how to use multilevel (hierarchical) models for dealing with nested data. We will discuss common topics in multilevel modeling, including sample size requirements, centering decisions, assumptions, and slopes and intercepts as fixed and random effects. Our focus will be on two and three level nested models, as well as growth modeling.
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