The performance of taxometric procedures and LVMMs has been evaluated separately under different conditions (Meehl, 1995, 1999; Cleland & Haslam, 1996; Haslam & Cleland, 1996, 2002; Lubke & Neale, 2006, 2008; Walters et al. 2010), but direct comparisons of the methods are sparse. The few published comparisons are usually limited to latent class analysis models. Some studies do not include a correct model representing dimensionality in the model comparisons, and are therefore not useful to decide whether a method can distinguish between dimensionality and taxonicity (Cleland et al. 2000; McGrath & Walters, 2012). The study by Lubke & Tueller (2010) includes more complex LVMMs but only covers MAXEIG, and does not evaluate misspecifications of the LVMM. However, taken together, the simulation studies seem to support the common sense intuition that if there is a priori knowledge about the covariance structure of the data, then integration of this knowledge in a mixture model can improve the power to distinguish latent classes. Furthermore, increasing misspecification of LVMMs can be expected to result in a deterioration of performance, as do violations of assumptions
