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.

Learn how to

• Use basic multilevel models.
• Use three-level and cross-classified models.
• Use generalized multilevel models for discrete dependent variables.

Who should attend

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

Formats available	Standard duration
Live Web Classroom:	4 half-day session(s)
e-Learning:	14 hours/180 day license

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.

This course addresses SAS/STAT software.

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.