IQ scores are volatile indices of global functional outcome, the final common path of an individual’s genes, biology, cognition, education, and experiences. In studying neurocognitive outcomes in children with neurodevelopmental disorders, it is commonly assumed that IQ can and should be partialed out of statistical relations or used as a covariate for specific measures of cognitive outcome. We propose that it is misguided and generally unjustified to attempt to control for IQ differences by matching procedures or, more commonly, by using IQ scores as covariates. We offer logical, statistical, and methodological arguments, with examples from three neurodevelopmental disorders (spina bifida meningomyelocele, learning disabilities, and attention deficit hyperactivity disorder) that: (1) a historical reification of general intelligence, g, as a causal construct that measures aptitude and potential rather than achievement and performance has fostered the idea that IQ has special status and that in studying neurocognitive function in neurodevelopmental disorders; (2) IQ does not meet the requirements for a covariate; and (3) using IQ as a matching variable or covariate has produced overcorrected, anomalous, and counterintuitive findings about neurocognitive function. (JINS, 2009, 15, 331–343.)

Purpose: Children with language impairment (LI) often perform below the level of typically developing peers on measures of both processing speed and working memory. This study examined the relationship between these 2 types of measures and attempted to determine whether such measures can account for the LI itself. Method: Fourteen-year-old children with LI and their typically developing peers participated in a wide range of processing speed and working memory tasks and were administered a comprehensive language test battery. Confirmatory factor analyses were used to compare 3 nested models designed to examine the dimensionality of the speed and working memory measures. A model that included a general speed factor was also evaluated. Results: The models meeting our evaluation criteria treated speed and working memory as separable factors. Furthermore, nonverbal as well as verbal processing factors emerged from these analyses. Latent variable regression analyses showed that each of the appropriate models accounted for 62% of the variance in the children’s concurrent composite language test scores, with verbal working memory making the largest contribution. Conclusions: These findings shed light on the relationship among different types of processing and suggest that processing factors can contribute to the understanding of language disorders.

Objective. The outcome in adolescence of children diagnosed as dyslexic during the early years of school was examined in children prospectively identified in childhood and continuously followed to young adulthood. This sample offers a unique opportunity to investigate a prospectively identified sample of adolescents for whom there is no question of the childhood diagnosis and in whom highly analytic measures of reading and language can be administered in adolescence. Design. Children were recruited from the Connecticut Longitudinal Study, a cohort of 445 children representative of those children entering public kindergarten in Connecticut in 1983. Two groups were selected when the children were in grade 9: children who met criteria for persistent reading disability in grades 2 through 6 (persistently poor readers [PPR]; n = 21) and a comparison group of nondisabled children, subdivided into average readers (n = 35) and superior readers (n = 39). In grade 9, each child received a comprehensive assessment of academic, language, and other cognitive skills. Results. Measures of phonological awareness (but not orthographic awareness) were most significant in differentiating the 3 reading groups, with smaller contributions from measures of word finding and digit-span. Academic measures that best separated good from poor readers were decoding and spelling, whereas measures of math and reading comprehension did not. Measures of phonological awareness, followed next by teacher rating of academic skills were the best predictors of decoding, reading rate, and reading accuracy. In contrast, the best predictor of reading comprehension was word finding, with digit span and socioeconomic status also contributing significantly. Using a growth curve model (quadratic model of growth to a plateau) all 3 groups demonstrated similar patterns of growth over time, with the superior group outperforming the average group, and the average group outperforming the PPR group. There was no evidence that the children in the PPR group catch up in their reading skills. Conclusions. Deficits in phonological coding continue to characterize dyslexic readers even in adolescence; performance on phonological processing measures contributes most to discriminating dyslexic and average readers, and average and superior readers as well. These data support and extend the findings of previous investigators indicating the continuing contribution of phonological processing to decoding words, reading rate, and accuracy and spelling. Children with dyslexia neither spontaneously remit nor do they demonstrate a lag mechanism for catching up in the development of reading skills. In adolescents, the rate of reading as well as facility with spelling may be most useful clinically in differentiating average from poor readers.