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• W2070479186 abstract "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." @default.
• W2070479186 created "2016-06-24" @default.
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• W2070479186 date "2010-01-01" @default.
• W2070479186 modified "2023-09-27" @default.
• W2070479186 title "Taxometrics, polytomous constructs, and the comparison curve fit index: A Monte Carlo analysis." @default.
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• W2070479186 doi "https://doi.org/10.1037/a0017819" @default.
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