The Prevalence of Dyslexia: A New Approach to Its Estimation

Abstract

How prevalent is dyslexia? A definitive answer to this question has been elusive because of the continuous distribution of reading performance and predictors of dyslexia and because of the heterogeneous nature of samples of poor readers. Samples of poor readers are a mixture of individuals whose reading is consistent with or expected based on their performance in other academic areas and in language, and individuals with dyslexia whose reading is not consistent with or expected based on their other performances. In the present article, we replicate and extend a new approach for determining the prevalence of dyslexia. Using model-based meta-analysis and simulation, three main results were found. First, the prevalence of dyslexia is better represented as a distribution that varies as a function of severity as opposed to any single-point estimate. Second, samples of poor readers will contain more expected poor readers than unexpected or dyslexic readers. Third, individuals with dyslexia can be found across the reading spectrum as opposed to only at the lower tail of reading performance. These results have implications for screening and identification, and for recruiting participants for scientific studies of dyslexia.

Keywords: Bayesian models; diagnosis; dyslexia; prevalence; reading disability.

Figure 1.

Funnel plot of residuals after regressing effect sizes on age.

Figure 2.

The distribution of the difference between listening comprehension and reading comprehension.

Figure 3.

Scatterplot of listening comprehension and reading comprehension. Points to the left of the vertical line represent scores of poor readers (i.e., the 20^th %-ile in RC). Points above the diagonal line represent readers with listening comprehension better than reading comprehension (i.e., at or above 1.5 standard deviations above the mean in LC-RC discrepancy score).