This course presents an applied approach on the use of mixed effects and/or multilevel models for clustered, repeated, and longitudinal experimental designs. Develops the skills to implement and interpret random effects, variance component models of time varying and time invariant predictors on outcome variables. Included topics: transitioning from general linear model to mixed effects model, interpretation of population-average and subject specific models containing random intercept and random slopes. Discussion of special topics including importance of graphing data, model fitting, centering, variance/covariance matrix, sample size, sample power, missing data in repeated measures designs. *Doctoral students will be required to complete an additional research related topic. Prerequisite: Instructor Approval.