Individual prediction of dyslexia by single vs. multiple deficit models.
2012 (English)In: Journal of Abnormal Psychology, ISSN 0021-843X, E-ISSN 1939-1846, Vol. 121, no 1, 212-224 p.Article in journal (Refereed) Published
The overall goals of this study were to test single versus multiple cognitive deficit models of dyslexia (reading disability) at the level of individual cases and to determine the clinical utility of these models for prediction and diagnosis of dyslexia. To accomplish these goals, we tested five cognitive models of dyslexia-two single-deficit models, two multiple-deficit models, and one hybrid model-in two large population-based samples, one cross-sectional (Colorado Learning Disability Research Center) and one longitudinal (International longitudinal Twin Study). The cognitive deficits included in these cognitive models were in phonological awareness, language skill, and processing speed and/or naming speed. To determine whether an individual case fit one of these models, we used two methods: 1) the presence or absence of the predicted cognitive deficits, and 2) whether the individuals level of reading skill best fit the regression equation with the relevant cognitive predictors (i.e., whether their reading skill was proportional to those cognitive predictors.) We found that roughly equal proportions of cases met both tests of model fit for the multiple deficit models (30-36%) and single deficit models (24-28%); hence, the hybrid model provided the best overall fit to the data. The remaining roughly 40% of cases in each sample lacked the deficit or deficits that corresponded with their best-fitting regression model. We discuss the clinical implications of these results for both diagnosis of school-age children and preschool prediction of children at risk for dyslexia.
Place, publisher, year, edition, pages
American Psychological Association , 2012. Vol. 121, no 1, 212-224 p.
IdentifiersURN: urn:nbn:se:liu:diva-73285DOI: 10.1037/a0025823ISI: 000300198500020OAI: oai:DiVA.org:liu-73285DiVA: diva2:470817