Intra-individual subtest variability on the Dutch Wechsler Intelligence Scales for Children-Revised (WISC-R(NL)) for children with learning disabilities, psychiatric disorders, and epilepsy

It is common practice to look at disparities among subtest scores ("scatter") on an intelligence test to establish if a score is deviant. However, it remains unclear whether subtest scatter reflects primarily normal variation within individuals or is clinically meaningful. The present study explored this issue based on data from 467 children with developmental disabilities tested on the Dutch WISC-R(NL). Of these children, 132 had learning disabilities, 178 had psychiatric disorders, and 157 had epilepsy. Subtest scatter was defined as scaled-score range (highest minus lowest scaled score). When contrasted with "normal scatter," the overall sample revealed higher ranges on the Performance Scale and Full Scale, although effect sizes were small. Analysis of the data for the three separate clinical samples revealed unusual scatter only for the sample of children with psychiatric disorders. When comparing the clinical samples, scaled-score range was larger for the sample of children with psychiatric disorders than for those with epilepsy. Two distinct subsamples revealed elevated ranges with moderate effect sizes: children with autistic spectrum disorders and children with left hemisphere seizures. These results suggest that elevated subtest scaled-score range might characterize specific clinical samples rather than denoting an overall sign of pathology.

Predicting LD on the basis of motivation, metacognition, and psychopathology: an ROC analysis

We examined how strongly motivation, metacognition, and psychopathology acted as predictors of learning disabilities (LD). The results from five studies suggested that level of motivation (as shown through self-efficacy, motivational force, task avoidance, goal commitment, or self-concept) was highly accurate in classifying students with or at risk for LD. Metacognition and psychopathology were also strong predictors. Classification accuracy using receiver operating characteristic (ROC) curves ranged between 77% and 96%. These rates were much higher than the chance-level (i.e., 50%-55%) rates sometimes yielded by cognitive indices. Linear discriminant function (LDF) analysis substantiated classification accuracy. These results suggest that motivation, metacognition, and psychopathology are strong predictors of LD. Understanding the influence of these characteristics may help researchers and practitioners more accurately screen and treat students with LD.

A nationwide epidemiologic modeling study of LD: risk, protection, and unintended impact

Through multiple logistic regression modeling, this article explores the relative importance of risk and protective factors associated with learning disabilities (LD). A representative national sample of 6- to 17-year-old students (N = 1,268) was drawn by random stratification and classified by the presence versus absence of LD in reading, spelling, and mathematics according to ability-achievement discrepancies or low achievement levels. The dichotomous classifications were regressed on sets of explanatory variables indicating potential biological, social-environmental, and cognitive factors, problem behavior, and classroom learning behavior. Modeling revealed patterns of high risk for male students and students evincing verbal and nonverbal ability problems and processing speed problems. It was shown that, absent controls for cognitive abilities (such as provided by the ability-achievement discrepancy definition), definitions keyed to low achievement will substantially overidentify ethnic minority and disadvantaged students and will be confounded by significantly higher proportions of students who display oppositional and aggressive behavior problems. Alternatively, good learning behaviors uniformly provide substantial reduction in the risk for LD.