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• W2182441148 abstract "Multilevel models (MLMs) are frequently used in social and health sciences where data are typically hierarchical in nature. However, the commonly used hierarchical linear models (HLMs) are only appropriate when the outcome of interest is normally distributed; when dealing with outcomes that are not normally distributed (i.e., binary, categorical, ordinal), a transformation and an appropriate error distribution for the response variable needs to be incorporated into the model and therefore, hierarchical generalized linear models (HGLMs) need to be used. This paper provides an" @default.
• W2182441148 created "2016-06-24" @default.
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• W2182441148 date "2014-01-01" @default.
• W2182441148 modified "2023-10-03" @default.
• W2182441148 title "Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX: The Basics" @default.
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• W2182441148 hasPublicationYear "2014" @default.
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