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. 2019 Aug;25(3):284-295.
doi: 10.1002/dys.1629. Epub 2019 Jul 22.

Are children with developmental dyslexia all the same? A cluster analysis with more than 300 cases

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Are children with developmental dyslexia all the same? A cluster analysis with more than 300 cases

David Giofrè et al. Dyslexia. 2019 Aug.

Abstract

Reading is vital to every aspect of modern life, exacerbated by reliance of the internet, email, and social media on the written medium. Developmental dyslexia (DD) characterizes a disorder in which the core deficit involves reading. Traditionally, DD is thought to be associated with a phonological impairment. However, recent evidence has begun to suggest that the reading impairment in some individuals is provoked by a visual processing deficit. In this paper, we present WISC-IV data from more than 300 Italian children with a diagnosis of DD to investigate the manifestation of phonological and visual subtypes. Our results indicate the existence of two clusters of children with DD. In one cluster, the deficit was more pronounced in the phonological component, while both clusters were impaired in visual processing. These data indicate that DD may be an umbrella term that encompasses different profiles. From a theoretical perspective, our results demonstrate that dyslexia cannot be explained in terms of an isolated phonological deficit alone; visual impairment plays a crucial role. Moreover, general rather than specific accounts of DD are discussed.

Keywords: Developmental dyslexia (DD); WISC-IV; dual-route model (DRC); specific learning disabilities (SLD); triangle model.

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Figures

Figure 1
Figure 1
(a) DRC model (e.g., Coltheart and al., 2001) and (b) Triangle model (e.g., Seidenberg & McClelland, 1989; Plaut, McClelland, Seidenberg, & Patterson, 1996)
Figure 2
Figure 2
Mean scores and standard errors estimated by the model for the indices Verbal Comprehension Index, Perceptual Reasoning Index, Working Memory Index (phonology), and Processing Speed Index (vision) on the two clusters. Cluster 1, N = 113 and Cluster 2, N = 203. Error bars represent 95% confidence intervals of the estimated mean values, computed via non‐parametric bootstrap (with 5,000 samples generation)

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