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

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

Introduction to Multilevel Models

• Nested data structures.
• Ignoring dependence.
• Methods for modeling dependent data structures.
• The random-effects ANOVA model.

Basic Multilevel Models

• Random-effects regression.
• Centering predictors in multilevel models.
• Model building.
• A comment on notation (self-study).
• Intercepts as outcomes.

Slopes as Outcomes and Model Evaluation

• Slopes as outcomes.
• Model assumptions.
• Model assessment and diagnostics.
• Maximum likelihood estimation.

The Analysis of Repeated Measures

• The conceptualization of a growth curve.
• The multilevel growth model.
• Time-invariant predictors of growth (self-study).
• Multiple groups models.

Three-Level and Cross-Classified Models

• Three-level models.
• Three-level models with random slopes.
• Cross-classified models.

Multilevel Models for Discrete Dependent Variables

• Discrete 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.

Before attending this course, you should: 

• Preferably, be familiar with the basic structure and concepts of SAS (for example, the DATA step and procedures). 
• Be familiar with concepts of linear models such as regression and ANOVA and with generalized linear models such as logistic regression. 
• Be familiar with linear mixed models to enhance understanding, although this is not necessary to benefit from the course.