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Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS – BHLM93

This course teaches students 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 generalised linear models (MGLM) and their appropriate use in a variety of settings.

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Duration:

3 days - Classroom

Learn how to

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

Who should attend?

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

Prerequisites

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 generalised 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.

Course Contents

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 conceptualisation of a growth curve
  • the multilevel growth model
  • modeling nonlinear change
  • time-invariant predictors of growth
  • 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
  • generalised linear models
  • multilevel generalised linear models
  • additional considerations

Generalised Multilevel Linear Models for Longitudinal Data

  • complexities of longitudinal data structures
  • the unconditional growth model for discrete dependent variables
  • conditional growth models for discrete dependent variables

Software Addressed

This course addresses SAS/STAT software.

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