The generalized linear mixed model (GLMM) is a statistical framework that broadens the traditional general linear model to include variables that are not normally distributed, relationships that are not strictly linear, and data that have dependency. The general linear model is the foundational statistical structure that includes almost all parametric statistical procedures such as linear regression and analysis of variance. Two of this model’s offshoots are the generalized linear model and the linear mixed model. This entry describes these three models and then explores their most flexible of offspring, the GLMM.
The general linear model is useful in answering research questions about the impact of one or multiple predictor variables on an outcome variable. A predictor variable is a variable that is being manipulated ...
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