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Title: Individual Prediction of Dyslexia by Single Versus Multiple Deficit Models
Contributor(s): Pennington, Bruce F (author); Santerre-Lemmon, Laura (author); Olson, Richard K (author); Rosenberg, Jennifer (author); MacDonald, Beatriz (author); Boada, Richard (author); Friend, Angela (author); Leopold, Daniel R (author); Samuelsson, Stefan (author); Byrne, Brian J  (author)orcid ; Willcutt, Erik G (author)
Publication Date: 2012
Open Access: Yes
DOI: 10.1037/a0025823Open Access Link
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Abstract: 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 individual's 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.
Publication Type: Journal Article
Source of Publication: Journal of Abnormal Psychology, 121(1), p. 212-224
Publisher: American Psychological Association
Place of Publication: United States of America
ISSN: 1939-1846
Field of Research (FOR): 170109 Personality, Abilities and Assessment
170199 Psychology not elsewhere classified
170101 Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)
Socio-Economic Outcome Codes: 930102 Learner and Learning Processes
930199 Learner and Learning not elsewhere classified
930103 Learner Development
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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