Detail kurzu

Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS(R)

EDU Trainings s.r.o.

Popis kurzu

This course teaches how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings. The self-study e-learning includes:Annotatable course notes in PDF format. Virtual lab time to practice.

Obsah kurzu

Introduction to Multilevel ModelsNested data structures.Ignoring dependence.Methods for modeling dependent data structures.The random-effects ANOVA model.Basic Multilevel ModelsRandom-effects regression.Centering predictors in multilevel models.Model building.A comment on notation (self-study).Intercepts as outcomes.Slopes as Outcomes and Model EvaluationSlopes as outcomes.Model assumptions.Model assessment and diagnostics.Maximum likelihood estimation.The Analysis of Repeated MeasuresThe conceptualization of a growth curve.The multilevel growth model.Time-invariant predictors of growth (self-study).Multiple groups models.Three-Level and Cross-Classified ModelsThree-level models.Three-level models with random slopes.Cross-classified models.Multilevel Models for Discrete Dependent VariablesDiscrete dependent variables.Generalized linear models.Multilevel generalized linear models.Additional considerations.Generalized Multilevel Linear Models for Longitudinal Data (Self-Study)Complexities of longitudinal data structures.The unconditional growth model for discrete dependent variables.Conditional growth models for discrete dependent variables.

Cílová skupina

Researchers in psychology, education, social science, medicine, and business, or others analyzing data with multilevel nesting structure
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