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

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.;It is recommended that you complete SAS Programming 1: Essentials and Statistics 2: ANOVA and Regression or have equivalent knowledge before taking this course.

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.