Taxometrics, polytomous constructs, and the comparison curve fit index: a Monte Carlo analysis.
The taxometric method effectively distinguishes between dimensional (1-class) and taxonic (2-class) latent structure, but there is virtually no information on how it responds to polytomous (3-class) latent structure. A Monte Carlo analysis showed that the mean comparison curve fit index (CCFI; Ruscio, Haslam, & Ruscio, 2006) obtained with 3 taxometric procedures-mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode)-accurately identified 1-class (dimensional) and 2-class (taxonic) samples and produced taxonic results when applied to 3-class (polytomous) samples. From these results it is concluded that using the simulated data curve approach and averaging across procedures is an effective way of distinguishing between dimensional (1-class) and categorical (2 or more classes) latent structure.
Walters
Glenn D
GD
Psychology Services, Federal Correctional Institution, Schuylkill, Minersville, PA 17954-0700, USA. gwalters@bop.gov
McGrath
Robert E
RE
Knight
Raymond A
RA
D006801:Humans; D009010:Monte Carlo Method; D011581:Psychological Tests / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*