Taxometrics, model-based clustering and LVMM differ with respect to their assumptions and to the type of a priori knowledge about the data that is required. Taxometrics rely on linear associations (i.e. covariances) between the observed variables and also zero covariances within clusters. Mild deviations from the assumption of zero within-cluster covariances have been reported to be unproblematic although explicit modeling of the within-cluster covariance structure can lead to superior power to detect taxonicity (Meehl, 1999; Ruscio & Ruscio, 2002; Lubke & Tueller, 2010; McGrath & Walters, 2012). Taxometrics aim at deciding between dimensionality on the one hand and a two-cluster solution (taxon and complement) on the other. The decisions are mainly based on graphical inspection, although quantitative measures have been proposed (Ruscio et al. 2007).
