Comparisons of methods with simulated data are very useful for quantifying a difference in power. Several simulation studies have been published that evaluate the performance of methods designed to distinguish between latent categories and latent dimensions (Cleland et al. 2000; Lubke & Tueller, 2010; McGrath & Walters, 2012). Unfortunately, the terminology used to describe the methods is not uniform. For instance, the term ‘finite mixture modeling’ has been used for model-based clustering and also for LVMM, and the terms ‘latent variable mixture modeling’ and ‘mixture modeling’ have been used to refer exclusively to latent class analysis. However, the latent class analysis model is a very constrained submodel within the more general LVMM framework that does not leverage the advantage of more complex LVMMs to model the factor structure within a class. The latter permits accounting for severity differences within a class. In addition to the ambivalent labeling of methods, the simulation studies differ greatly with respect to the data generation. As described earlier, characteristics of the data have a direct impact on the inference regarding categories and dimensions. To evaluate
