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.
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS(R)
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.
• 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
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
This course addresses SAS/STAT software.
This is a QA approved partner course
Face-to-face learning in the comfort of our quality nationwide centres, with free refreshments and Wi-Fi.
Find dates and prices
Online booking is currently not available for this course, to find out more please call us on 0113 220 7150 or email us at email@example.com to discuss how we can help.
Fully accredited to ensure we provide the highest possible standards in learning