In the models discussed here, there is a hierarchy of variation that corresponds to groupings within the data. For example, students may be sampled from different classes, that in turn are sampled from different schools. Or, rather than being nested, groups may be crossed. Important notions are those of fixed and random effects, and variance components. Analysis of data from designs that have the balance needed to allow an analysis of variance breakdown are a special case. Further types of mixed models are generalized linear mixed models and repeated measures models. Repeated measures models are multilevel models where measurements consist of multiple profiles in time or space, resulting in time or spatial dependence. Relative to the length of time series that is required for a realistic analysis, each individual repeated measures profile can and often will have values for a few time points only.