Generalized linear models are an extension, or generalization, of the linear modeling process which allows for non-normal distributions. Common non-normal distributions are Poisson, Binomial, and Multinomial. Related linear models include ANOVA, ANCOVA, MANOVA, and MANCOVA, as well as the regression models. In SPSS, generalized linear models can be performed by selecting “Generalized Linear Models” from the analyze of menu, and then selecting the type of model to analyze from the Generalized Linear Models options list.
Generalized Estimating Equations extends Generalized Linear Models further by involving dependent data such as, repeated measures, logistic regression and other various models involving correlated data. In SPSS, Generalized Estimating Equations can be done by selecting “Generalized Linear Models” from the analyze menu, and then selecting the “Generalized Estimating Equations” from the Generalized Linear Models options list.
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The dependent variable in the implementation of Generalized Estimating Equations and Generalized Linear Models are distributed in the following distributions:
There are certain assumptions in Generalized Estimating Equations and Generalized Linear Models. These assumptions are as follows:
Generalized Linear Model Resources
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