ABSTRACT
When outcome variables are severely non-normal, the usual remedy is to try to normalize the data using a nonlinear transformation, to use robust estimation methods, or by a combination of these (see Chapter 4 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 6. This chapter describes the use of the generalized linear model for ordered categorical data and for count data.
