There is a newer version of this course. Please see the schedule for the new Mixed Models Analyses Using SAS course.
This course teaches you how to analyze linear mixed models using PROC MIXED. A brief introduction to analyzing generalized linear mixed models using PROC GLIMMIX is also included.
Learn how to
- analyze data (including binary data) with random effects
- fit random coefficient models and hierarchical linear models
- analyze repeated measures data
- obtain and interpret the best linear unbiased predictions
- perform residual and influence diagnostic analysis
- deal with convergence issues.
Who should attend
Statisticians, experienced data analysts, and researchers with sound statistical knowledge
Before attending this course, you should
- know how to create and manage SAS data sets
- have experience performing analysis of variance using the GLM procedure of SAS/STAT software
- have completed and mastered the Statistics 2: ANOVA and Regression course or completed a graduate-level course on general linear models.
Exposure to mixed models and matrix algebra will enhance your understanding of the material. Some experience manipulating SAS data sets and producing graphs using SAS/GRAPH software is also recommended.
This course addresses SAS/STAT software.
Introduction to Mixed Models
Examples of Mixed Models in Some Designed Experiments
- identifying fixed and random effects
- describing linear mixed model equations and assumptions
- fitting a linear mixed model for a randomized complete block design using the MIXED procedure
- writing CONTRAST and ESTIMATE statements to perform custom hypothesis tests
Examples of Mixed Models with Covariates
- fitting a linear mixed model for two-way mixed models
- fitting a linear mixed model for nested mixed models
- fitting a linear mixed model for split-plot designs
- fitting a linear mixed model for crossover designs
Best Linear Unbiased Prediction
- fitting analysis of covariance models with random effects
- performing random coefficient regression analysis
- conducting hierarchical linear modeling
Repeated Measures Analysis
- explaining BLUPs and EBLUPs
- producing parameter estimates associated with the fixed effects and random effects
- explaining the difference between LSMEANS and EBLUPs
- computing LSMEANS and EBLUPs using the MIXED procedure
Mixed Models Residual Diagnostics and Troubleshooting
- discussing issues on repeated measures analysis, including modeling covariance structure
- analyzing repeated measures data using the four-step process with the MIXED procedure
Additional Information about Linear Mixed Models (Self-Study)
- performing residual and influence diagnostics for linear mixed models
- troubleshooting convergence problems
Introduction to Generalized Linear Mixed Models and Nonlinear Mixed Models
- discussing issues associated with unbalanced data, data with empty cells, estimation and inference of variance parameters, and different denominator degrees of freedom estimation methods
- discussing the situations where generalized linear mixed models and nonlinear mixed models analysis are needed
- performing the analysis for generalized linear mixed models using the GLIMMIX procedure