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

A Monte Carlo evaluation of four procedures for detecting taxonicity was conducted using artificial data sets that were either taxonic or nontaxonic. The data sets were analyzed using two of Meehl's taxometric procedures, MAXCOV and MAMBAC, Ward's method for cluster analysis in concert wit...

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Bibliographic Details
Published in:Psychological reports 2000-08, Vol.87 (1), p.37-47
Main Authors: Cleland, C M, Rothschild, L, Haslam, N
Format: Article
Language:English
Subjects:
Cluster Analysis
Data Interpretation, Statistical
Humans
Models, Statistical
Monte Carlo Method
Psychometrics - methods
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Summary:A Monte Carlo evaluation of four procedures for detecting taxonicity was conducted using artificial data sets that were either taxonic or nontaxonic. The data sets were analyzed using two of Meehl's taxometric procedures, MAXCOV and MAMBAC, Ward's method for cluster analysis in concert with the cubic clustering criterion and a latent variable mixture modeling technique. Performance of the taxometric procedures and latent variable mixture modeling were clearly superior to that of cluster analysis in detecting taxonicity. Applied researchers are urged to select from the better procedures and to perform consistency tests.
ISSN:0033-2941
1558-691X
DOI:10.2466/PR0.87.5.37-47