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

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...

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$2,000 USD GSA  $1,600.96
Course Code BHLM42
Duration 2 days
Available Formats Classroom
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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.

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

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 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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