As currently defined, it is not clear whether Nonverbal Learning
Disabilities (NLD) should be considered a matter of kind or magnitude
(Meehl, 1995). The taxonicity of NLD, or the
degree to which it is best construed as discrete versus
continuous, has not been investigated using methods devised for this
purpose. Latent Class Analysis (LCA) is a method for finding subtypes of
latent classes from multivariate categorical data. This study represents
an application of LCA on a sample of children and adolescents with spina
bifida myelomeningocele (SBM) (N = 44), those presenting with
features of NLD (N = 28) but no medical condition, and control
volunteers (N = 44). The two-class solution provided evidence for
the presence of a taxon with an estimated base-rate in the SBM group of
.57. Indicator validities (the conditional probabilities of indicator
endorsement in each latent class) suggest a somewhat different priority
for defining NLD than is typically used by researchers investigating this
disorder. A high degree of correspondence between LCA classifications and
those based on a more conventional algorithm provided evidence for the
validity of this approach. (JINS, 2007, 13,
50–58.)