It is often difficult to interpret taxometric graphs because factors other than the construct's latent structure (e.g., skew, correlations among the indicators) can influence the shape of these graphs. To guide the interpretation of taxometric graphs, one strategy that has received considerable empirical support is to simulate data sets that reproduce many of the features of the actual data while varying its latent structure (taxonic or dimensional). These simulated data sets can then be analyzed using MAMBAC, MAXEIG, and L-Mode (Ruscio et al., 2007), and the graphs can be compared to the graphs from the research data. In each of the present studies, we generated 100 samples of simulated taxonic and dimensional comparison data and used comparison curve fit indices (CCFI) to assess the goodness-of-fit between the research data and the simulated taxonic and dimensional graphs. CCFI values greater than .55 support a taxonic structure, and those less than .45 support a dimensional latent structure. In a Monte Carlo study using 100,000 data sets, the latent structure was correctly classified 99.9% of the time when the three taxometric procedures all
