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

Course Details
Code: BHLM42
Tuition (USD): $1,650.00 • Classroom (2 days)

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.

Skills Gained

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

Who Can Benefit

  • 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.

Course Details

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
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