Mixed Models Analyses Using SAS(R)

Course code: AGLM42

This course teaches you how to analyze linear mixed models using the MIXED procedure. A brief introduction to analyzing generalized linear mixed models using the GLIMMIX procedure is also included.
1 800 EUR

2 178 EUR including VAT

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Course dates

Starting date: Upon request

Type: Upon request

Course duration: 21 hours

Language: en

Price without VAT: 1 800 EUR


Type Course
Language Price without VAT
Upon request Upon request 21 hours en 1 800 EUR Register
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Target group

Statisticians, experienced data analysts, and researchers with sound statistical knowledge

Course structure

Introduction to Mixed Models

  • 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 in Some Designed Experiments

  • 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

Examples of Mixed Models with Covariates

  • fitting analysis of covariance models with random effects
  • performing random coefficient regression analysis
  • conducting hierarchical linear modeling

Best Linear Unbiased Prediction

  • 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

Repeated Measures Analysis

  • discussing issues on repeated measures analysis, including modeling covariance structure
  • analyzing repeated measures data using the four-step process with the MIXED procedure

Mixed Models Residual Diagnostics and Troubleshooting

  • performing residual and influence diagnostics for linear mixed models
  • troubleshooting convergence problems

Additional Information about Linear Mixed Models (Self-Study)

  • discussing issues associated with unbalanced data, data with empty cells, estimation and inference of variance parameters, and different denominator degrees of freedom estimation methods

Introduction to Generalized Linear Mixed Models and Nonlinear Mixed Models

  • 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


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
  • have an understanding of generalized linear models and their analysis.
  • Do you need advice or a tailor-made course?


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