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Cylindrical data are multivariate data which consist of a directional, in this paper circular, and a linear component. Examples of cylindrical data in psychology include human navigation (direction and distance of movement), eye-tracking research (direction and length of saccades) and data from an interpersonal circumplex (location and intensity on the IPC). In this paper we adapt four models for cylindrical data to include a regression of the circular and linear component onto a set of covariates. Subsequently, we illustrate how to fit these models and interpret their results on a dataset on the interpersonal behavior of teachers.