In this module we discuss how to analyze dependent data, that is, data for which the assumption of independence needed in Linear Models is violated. Examples of situations where dependence is important are: repeated measurements, e.g. several observations on the same subject, the same plant, the same pen, the same cage,, split-plot experiments and other nested designs, e.g. data on animals in the same pen within the same farm or leaves of the same plant in the same pot within the same greenhouse, subsampling and pseudo-replication. In computer sessions participants can practice fitting models of this type, and gain an understanding of the output created by the software.