Hostname: page-component-7c8c6479df-24hb2 Total loading time: 0 Render date: 2024-03-28T19:58:46.477Z Has data issue: false hasContentIssue false

Taxonicity of nonverbal learning disabilities in spina bifida

Published online by Cambridge University Press:  13 December 2006

M. DOUGLAS RIS
Affiliation:
Department of Pediatrics and Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
ROBERT T. AMMERMAN
Affiliation:
Department of Pediatrics and Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
NIELS WALLER
Affiliation:
Department of Psychology, University of Minnesota, Minneapolis, Minnesota
NICOLAY WALZ
Affiliation:
Department of Pediatrics and Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
SONYA OPPENHEIMER
Affiliation:
Department of Pediatrics and Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
TANYA MAINES BROWN
Affiliation:
Department of Pediatrics and Columbus Children's Hospital, Columbus, Ohio
BENEDICTA G. ENRILE
Affiliation:
Department of Pediatrics and Columbus Children's Hospital, Columbus, Ohio
KEITH OWEN YEATES
Affiliation:
Department of Pediatrics and Columbus Children's Hospital, Columbus, Ohio

Abstract

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.)

Type
Research Article
Copyright
© 2007 The International Neuropsychological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aitkin, M., Anderson, D., & Hinde, J. (1981). Statistical modeling of data on teaching styles (with discussion). Journal of the Royal Statistical Society A., 144, 419461.Google Scholar
Benton, A., Hamsher, K., Varney, N., & Spreen, O. (1983). Judgement of line orientation. In Contributions to neuropsychological assessment: A clinical manual (pp. 5364). Oxford: Oxford University Press.
Boll, T. (1993). Children's Category Test. San Antonio, TX: Psychological Corporation.
Brown, T.M., Ris, M.D., Beebe, D.W., Ammerman, R.T., Oppenheimer, S., Enrile, B.G., & Yeates, K.D. (2007). Factors of biological risk and reserve associated with executive behaviors in children and adolescents with spina bifida myelomeningocele. Child Neuropsychology, (in press).Google Scholar
Bucholz, K.K., Hesselbrock, V.M., Heath, A.C., Kramer, J.R., & Schuckit, M.A. (2000). A latent class analysis of antisocial personality disorder symptom data from a multi-centre family study of alcoholism. Addiction, 95, 553567.Google Scholar
Buono, L.A., Morris, M.K., Morris, R.D., Krawiecki, N., Norris, F.H., Foster, M.A., & Copeland, D.R. (1998). Evidence for the syndrome of nonverbal learning disabilities in children with brain tumors. Child Neuropsychology, 4, 144158.Google Scholar
Cleland, C.M., Rothschild, L., & Haslem, (2000). Detecting latent taxa: Monte Carlo comparison of taxometric, mixture model, and clustering procedures. Psychological Reports, 87, 3747.Google Scholar
Clogg, C.C. (1995). Latent class models. In G. Arminger, C.C. Clogg, & M.E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311359). New York: Plenum.
Collins, L.M., Fidler, P.L., Wugalter, S.E., & Long, J.D. (1993). Goodness of fit testing for latent class models. Multivariate Behavioral Research, 28, 375389.Google Scholar
Cressie, N. & Read, T.R.C. (1984). Multinomial goodness of fit tests. Journal of the Royal Statistical Society, B, 46, 440464.Google Scholar
Dayton, C.M. (1999). Latent class scaling analysis. Sage University Paper Series on Quantitative Applications in the Social Sciences. 07–126. Thousand Oaks, CA: Sage.
De Menzes, L.M. (1999). On fitting latent class models for binary data. British Journal of Mathematical and Statistical Psychology, 52, 149168Google Scholar
Denckla, M.B. (1978). Minimal brain dysfunction. In J. Chall & A. Mirsky (Eds.), Education and the brain (pp. 223268). Chicago: National Society for the Study of Education and University of Chicago Press.
Dennis, M., Edelstein, K., Frederick, J., Copeland, K., Francis, D., Blaser, S.E., Kramer, L.A., Drake, J.M., Brandt, M., Hetherington, R., & Fletcher, J.M. (2005). Peripersonal spatial attention in children with spina bifida: Associations between horizontal and vertical line bisection and congenital malformations of the corpus callosum, midbrain, and posterior cortex. Neuropsychologia, 43, 20002010.Google Scholar
Donders, J., Rourke, B.P., & Canaday, A.I. (1991). Neuropsychological functioning of hydrocephalic children. Journal of Clinical and Experimental Neuropsychology, 13, 607613.Google Scholar
Duncan, A.E., Neuman, R.J., Kramer, J., Kuperman, S., Hesselbrock, V., Reich, T., & Bucholz, K.K. (2005). Are there subgroups of bulimia nervosa based on comorbid psychiatric disorders? International Journal of Eating Disorders, 37, 1925.Google Scholar
Efron, B.S. (1984). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37, 3648.Google Scholar
Erlenmeyer-Kimling, L., Golden, R.R., & Cornblatt, B.A. (1989). A taxometric analysis of cognitive and neuromotor variables in children at risk for schizophrenia. Journal of Abnormal Psychology, 98, 203208.Google Scholar
Everitt, B.S. (1984). A note on parameter estimation for Lazarsfeld's latent class model using the EM algorithm. Multivariate Behavioral Research, 19, 7989.Google Scholar
Ewing-Cobbs, L., Fletcher, J.M., Levin, H.S., & Boudousquie, A. (1993). Nonverbal learning disabilities in children and adolescents following closed-head injury. Journal of Clinical and Experimental Neuropsychology, 15, 41.Google Scholar
Fletcher, J.M., Brookshire, B.L., Bohan, T.P., Brandt, M.E., & Davidson, K.C. (1995). Early Hydrocephalus. In B. P. Rourke (Ed.), Syndrome of nonverbal learning disabilities (pp. 206238). New York: Guildford Press.
Fletcher, J.M., Dennis, M., & Northrup, H. (2000). Hydrocephalus. In K.O. Yeates, M.D. Ris, & H.G. Taylor (Eds.), Pediatric neuropsychology: Theory, research, and practice (pp. 2546). New York: Guilford Press.
Fletcher, J.M., Copeland, K., Frederick, J.A., Blaser, S.E., Kramer, L.A., Northrup, H., Hannay, H.J., Brandt, M.E., Francis, D.J., Villarreal, G., Drake, J.M., Laurent, J.P., Townsend, I., Inwood, S., Boudousquie, A., & Dennis, M. (2005). Spinal lesion level in spina bifida: A source of neural and cognitive heterogeneity. Journal of Neurosurgery, 102, 268279.Google Scholar
Fraley, R.C. & Spieker, S.J. (2003). Are infant attachment patterns continuously or categorically distributed? A taxometric analysis of strange situation behavior. Developmental Psychology, 39, 387404.Google Scholar
Freeman, M.F. & Tukey, J.W. (1950). Transformations related to the angular and square root. Annals of Mathematics and Statistics, 21, 607611.Google Scholar
Golden, R.R. & Mayer, M.J. (1995). Peaked indicators: A source of pseudotaxonicity of a latent trait. In D. Lubinski & R.V. Dawis (Eds.), Assessing individual differences in human behavior (pp. 93115). Palo Alto, CA: Davies-Black Publishing.
Goodman, L.A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215231.Google Scholar
Harnadek, M.C. & Rourke, B.P. (1994). Principal identifying features of the syndrome of nonverbal learning disabilities in children. Journal of Learning Disabilities, 27, 144154.Google Scholar
Hommeyer, J.S., Holmbeck, G.N., Willis, K.E., & Coers, S. (1999). Condition severity and psychosocial functioning in pre-adolescents with spina bifida: Disentangling proximal functional status and distal adjustment outcomes. Journal of Pediatric Psychology, 24, 499509.Google Scholar
Klove, H. (1963). Clinical neuropsychology. In F.M. Forster (Ed.), The medical clinics of North America (pp. 16471658). New York: Sanders.
Langeheine, R., Pannekoek, J., & van de Pol, F. (1996). Bootstrapping goodness of fit measures in categorical data analysis. Sociological Methods and Research, 24, 492516.Google Scholar
MacCallum, R.C., Zhang, S., Preacher, K.J., & Rucker, D.D. (2002). On the practice of dichotomization of qualitative variables. Psychological Methods, 7, 1940.Google Scholar
McCutcheon, A.C. (1987). Latent class analysis. Beverly Hills: Sage Publications.
Meehl, P.E. (1995). Bootstraps taxometrics: Solving the classification problem in psychopathology. American Psychologist, 50, 266274.Google Scholar
Meehl, P.E. (2004). What's in a taxon? Journal of Abnormal Psychology, 24, 499509.Google Scholar
Murphy, E.A. (1964). One cause? Many causes? The argument from the bimodal distribution. Journal of Chronic Disease, 17, 301324.Google Scholar
Myklebust, H.R. (1975). Nonverbal learning disabilities: Assessment and intervention. In H.R. Myklebust (Ed.), Progress in learning disabilities (Vol. 3, pp. 85121). New York: Grune & Stratton.
Nowicki, S., Jr. & Duke, N.P. (1994). Individual differences in the non-verbal communication of affect: The Diagnostic Analysis of Nonverbal Accuracy (DANVA) Scale. Journal of Nonverbal Communication, 18, 918.Google Scholar
Pelletier, P.M., Ahmad, S.A., & Rourke, B.P. (2001). Classification rules for basic phonological processing disabilities and nonverbal learning disabilities: Formulation and external validity. Child Neuropsychology, 7, 8498.Google Scholar
Pennington, B.F. (1991). Diagnosing learning disorders: A neuropsychological framework. New York: Guilford.
R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Reigel, D.H., & Rotenstein (1994). Spina bifida. In W. R. Cheek (Ed.), Pediatric Neurosurgery (3rd ed.) (pp. 5176). Philadelphia: W. B. Saunders.
Reitan, R.M. & Wolfson, D. (1985). The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation. Tuscon, AZ: Neuropsychology Press.
Reynolds, C.R. & Kamphaus, R.W. (1992). Behavioral assessment system for children manual. Circle Pines, MN: American Guidance Service, Inc.
Rindskopf, R. & Rindskopf, W. (1986). The value of latent class analysis in medical diagnosis. Statistics in Medicine, 5, 2127.Google Scholar
Ris, M.D. & Nortz, M., (in press). Nonverbal learning disorder. In J. Morgan & J. Ricker (Eds.), Handbook of clinical neuropsychology. New York: Taylor & Francis Publishing.
Rourke, B.P. (1987). Syndrome of nonverbal learning disabilities: The final common pathway of white matter disease/dysfunction. The Clinical Neuropsychologist, 1, 209234.Google Scholar
Rourke, B.P. (1995). Syndrome of nonverbal learning disabilities: Neurodevelopmental manifestations. New York: The Guildford Press.
Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology, 7, 273295.Google Scholar
Szatmari, P., Volkmar, F., & Walter, S. (1995). Evaluation of diagnostic criteria for autism using latent class models. Journal of the American Academy of Child and Adolescent Psychiatry, 34, 216222.Google Scholar
Titterington, D.M., Smith, A.F.M., & Makov, U.E. (1985). Statistical analysis of finite mixture distributions. New York: Wiley.
Voeller, K.K. (1986). Right-hemisphere deficit syndrome in children. American Journal of Psychiatry, 143, 10041009.Google Scholar
Waller, N.G., Putnam, F.W., & Carlson, E.B. (1996). Types of dissociation and dissociative types: A taxometric analysis of dissociative experiences. Psychological Methods, 1, 300321.Google Scholar
Waller, N.G. (2004). LCA 1.1 An R package for exploratory latent class analysis. Applied Psychological Measurement, 28, 141142.Google Scholar
Wechsler, D. (1991). Manual for the Wechsler Intelligence Scale for Children–Third Edition. San Antonio, TX: The Psychological Corporation.
Wechsler, D. (1992). The Wechsler Individual Achievement Test. San Antonio, TX: The Psychological Corporation.
Wechsler, D. (1997). The Wechsler Adult Intelligence Scale–3rd ed. San Antonio, TX: The Psychological Corporation.
Yeates, K.O., Loss, N., Colvin, A.N., & Enrile, B.G. (2003). Do children with myelomeningocele and hydrocephalus display nonverbal learning disabilities? An empirical approach to classification. Journal of the International Neuropsychological Society, 9, 653662.Google Scholar
Young, M.A. (1983). Evaluating diagnostic criteria: A latent class paradigm. Journal of Psychiatric Research, 17, 285296.Google Scholar