Multivariate taxometric procedures: distinguishing types from continua
Can taxometric procedures be used to distinguish types (species, latent classes, taxa) from continua (dimensions, latent traits, factors); and, if so, how? Aimed at demystifying this process, Waller and Meehl unpack Meehl's work on the MAXCOV-HITMAX procedure to reveal the underlying rationale of MAXCOV in simple terms and show how this technique can be profitably used in a variety of disciplines by researchers in their taxonomic work.
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Series Editors Introduction
Mathematical Foundations of Multivariate Taxometrics
A Multivariate Generalization
8 other sections not shown
4J H 4J id causal Chapter cluster analysis complement class component scores consistency tests corroborated covariance matrix Covariance Mixture Theorem data sets defined denote density plot denying the antecedent derive dimensional dissociative identity disorder distribution eigenvalues equal factor analysis factor loadings factor scores function H 4J H id H rH hitmax id 4J id id indicator means input scores L-Mode L. L. Thurstone latent class mathematical MAXCOV MAXCOV plot MAXCOV-HITMAX MAXEIG plots Meehl & Yonce method multivariate nontaxon members nuisance covariance output covariances parameter estimates Paul Meehl positive rate predicted probands profile similarity programs psychometric Q correlations Q-factor regression rH H rH id rH rH standard deviation standard scores statistics taxa taxometric procedures taxon and complement taxon base rate taxon class taxon indicators taxon members taxon membership taxonic model taxonic parameters taxonic structure techniques theory variance Waller within-class x-slab