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Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS

Business Knowledge Series course

This course is not currently scheduled.

Duration: 3.0 days
Course fee:
EPTO units: .
CEUs: 1.8

Presented by Daniel Bauer or Patrick Curran, Professors at the University of North Carolina

This course teaches students how to identify complex and dynamic relations within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel nonlinear models (MNLM) and their appropriate use in a variety of settings.

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Who should attend

Researchers in psychology, education, social science, medicine, and business

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Prerequisites
Before attending this course, it would be beneficial to be familiar with the basic structure and concepts of the SAS System (DATA step, procedures). However, all of the SAS code needed to estimate the multilevel models and multilevel nonlinear models is presented in the course. Students should be familiar with core concepts in descriptive statistics, multiple regression, and analysis of variance. Familiarity with generalized linear models will enhance understanding but is not necessary to benefit from the course as a whole.
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
  • 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
  • modeling nonlinear change
  • time-invariant predictors of growth
  • multiple groups models
Three-Level and Cross-Classified Models (Self-Study)
  • 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
  • complexities of longitudinal data structures
  • the unconditional growth model for discrete dependent variables
  • conditional growth models for discrete dependent variables
Software
This course addresses SAS/STAT.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

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Course fee and EPTO units will differ for on-site training.

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