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 of taxometric procedures or deviations from assumed distributions in model-based clustering and LVMM (Lubke & Neale, 2008; Ruscio & Kaczetow, 2009; Lubke & Tueller, 2010). Most simulations agree on the main factors that lead to deterioration of performance, irrespective of the specific method used. Not surprisingly, these are effect size (i.e. distance between clusters) and sample size in the smallest cluster. The response format of the items (continuous works better than binary), the reliability of the observed items (higher is better) and the complexity of the within-class model (simpler models require less parameters to be estimated) can also affect the power to discriminate between classes (Nylund et al. 2007; Lubke & Tueller, 2010; Tueller & Lubke, 2010).
