Transcription of The Multilevel Generalized Linear Model for …
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The Multilevel Generalized Linear Model for …
1 7 The Multilevel Generalized Linear Model for Categorical and Count Data When outcome variables are severely non-normal, the usual remedy is to try to normalize the data using a non- Linear transformation, to use robust estimation methods, or a combination of these (see Chapter Four for details). Then again, just like dichotomous outcomes, some types of data will always violate the normality assumption. Examples are ordered (ordinal) and unordered (nominal) categorical data, which have a uniform distribution, or counts of rare events. These outcomes can sometimes also be transformed, but they are preferably analyzed in a more principled manner, using the Generalized Linear Model introduced in Chapter Six. This chapter describes the use of the Generalized Linear Model for ordered categorical data and for count data. ORDERED CATEGORICAL DATA There is a long tradition, especially in the social sciences, of treating ordered categorical data as if they were continuous and measured on an interval scale.
Multilevel Analysis: Techniques and Applications 3 Figure 7.1 Thresholds and observed responses for logit and probit model. The model in 7.1 is often called a proportional odds model because it is assumed that the effect of
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