The identification and classification of reading difficulties based on the Simple View of Reading

The identification and classification of reading difficulties based on the Simple View of Reading

First Author: Mike Sleeman -- University of Canterbury
Additional authors/chairs: 
John Everatt; Alison Arrow; Amanda Arrow
Keywords: simple view of reading, Dyslexia, Comprehension difficulty, Reading disability, Reading Assessment
Abstract / Summary: 

Research indicates that poor readers are not a homogeneous group of students. This study used the Simple View of Reading (SVR) to classify poor readers into one of four poor reader categories. A strengths and weaknesses profile analysis was also conducted to determine whether it is possible to discriminate between the poor reader categories across a range of reading related measures.

This study included 200 children in Years 4, 5, and 6 (8-10 years-of-age) from New Zealand primary schools. The students completed 14 individually administered tests that assessed various aspects of their reading comprehension, decoding, linguistic comprehension, phonological awareness, and rapid automized naming ability. Cut-off lines were used to classify students according to their performance on composite decoding and linguistic comprehension variables. Comparisons were then made between the groups on all the measures.

The results provide support for a four-group classification system based on the SVR: dyslexia = 17%, mixed = 23%, specific comprehension difficulty = 25%, unexplained = 35%. These results are consistent with USA studies that have used a similar classification approach. The profile analysis revealed that the four poor reader groupings demonstrated significantly different strengths and weaknesses across the study measures that were generally consistent with SVR-related models.

The results confirm that the SVR can be used to classify students according to the aetiology of their reading difficulty. The profile analysis, which was not conducted in previous classification studies, will be discussed along with alternative grouping and clustering procedures.