The “What, Why, and How” of Generalized Linear Mixed Models



1:00 - 2:00 p.m. ET

Generalized Linear Mixed Models (GLMM) are a powerful and flexible class of statistical models used in a variety of applications. This modeling framework accommodates a variety of situations many analysts find they have with their data, providing the reasoning to extend traditional linear modeling to more modern GLMM. 

A variety of experiment designs include some additional random effects other than "residual”, requiring mixed model methodology for analysis. When the response is clearly non-Gaussian, the Generalized Linear Model (GLM) is appropriate, but JMP has not previously included random effects in any of its GLM-type platforms. Thus, the GLMM is now available. 

Join us Sept. 29 to hear our speakers discuss the history and development of GLMM and why they are important to researchers. They’ll also show the implementation of GLMMs using examples representative of real-world experiments. 

Meet the speakers

Walt Stroup

Walt Stroup

Emeritus Professor, University of Nebraska-Lincoln

Stroup served on the University of Nebraska faculty from 1979 until 2020, having taught statistical modeling and design of experiments, and conducting research of mixed models and their applications in agriculture, natural resources, medical and pharmaceutical sciences, education, and the behavioral sciences. In 2020, he received the University of Nebraska’s Outstanding Teaching and Innovative Curriculum Award, the university’s highest teaching honor. Stroup co-authored SAS for Mixed Models, SAS for Linear Models, 4th ed., and authored Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. He is a Fellow of the American Statistical Association.

Elizabeth A. Claassen

Elizabeth A. Claassen

Senior Research Statistician Developer, JMP Statistical Discovery LLC

Dr. Claassen has more than a decade of experience using JMP and SAS for mixed modeling. Her chief interest is generalized linear mixed models (GLMM), and she is lead developer on the new GLMM capabilities in JMP, in addition to maintaining and improving existing mixed model platform. Dr. Claassen earned an M.S. and Ph.D. in statistics from the University of Nebraska–Lincoln, where she received the Holling Family Award for Teaching Excellence from the College of Agricultural Sciences and Natural Resources.


After their talks, Stroup and Claassen will join Anne Milley for an in-depth discussion on the importance of why GLMM development is important to researchers.