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.
|Original language||English (US)|
|Number of pages||9|
|Journal||Journal of the International Neuropsychological Society|
|State||Published - Jan 2007|
- Developmental disabilities
- Learning disorders
- Neural tube defects