Detecting latent taxa: Monte Carlo comparison of taxometric, mixture model, and clustering procedures

A Monte Carlo evaluation of four procedures for detecting taxonicity was co nducted using artificial data sets that were either taxonic or nontaxonic T he data sets were analyzed using two of Meehl's taxometric procedures, MAXC OV and MAMBAC, Ward's method for cluster analysis in concert with the cubic clustering criterion and a latent variable mixture modeling technique. Per formance of the taxometric procedures and latent variable mixture modeling were clearly superior to chat of cluster analysis in detecting taxonicity. Applied researchers are urged to select from the better procedures and to p erform consistency rests.