Modelling Binary and Categorical Repeated Measurement Data
Just as with normal repeated measurement data, there are different ways to approach the analysis of repeated binary, categorical and count data. Two popular approaches are marginal models and subject effects models, and the assumptions underlying each of these are different. This course concentrates on these two approaches. The theory underlying the methods, what the differences are, and how to fit and interpret the models are all covered.
Although the course places most of its emphasis on binary and count data, it also shows how the approaches can be extended to categorical data. Practical examples are used to illustrate the methods, and participants have the opportunity to fit and interpret models themselves.
Who Should Attend?
Statisticians. No previous experience of analysing repeated non-normal data is required, but some knowledge of general linear modelling and generalised linear models will be assumed. Familiarity with methods for normal repeated measurement data is useful but not essential.
How You Will Benefit
This course will provide you with practical skills for tackling the analysis of repeated binary, categorical and count data.
What Do We Cover?
This course has practical exercises written for: SAS
Note: For practical work participants must bring their own laptop with a full licensed version of the software.
Related Courses: Repeated Measurements Analysis; Generalised Linear Mixed Models.