Co-occurrence of Reading Disabilities and Math Disabilities: A Meta-Analysis.

Despite the importance of identifying individuals with reading disabilities, existing operational definitions of reading disability do not result in reliable identification. A large part of the problem arises from measurement error when a cut-point is imposed on a continuous distribution, especially for low base-rate conditions. One way to reduce measurement error is to include additional predictors in reading disability models. The present study examined co-occurring math disability as a possible additional criterion for predicting reading disability. Meta-analysis was used to examine the probability of individuals with reading disability also having a comorbid math disability. Possible moderators including age, severity of disability, and language were examined. The main result was an average weighted odds ratio of 2.12, 95% confidence interval [1.76, 2.55], indicating that students with a math disability are just over two times more likely to also have a reading disability than those without a math disability. Implications of the results are discussed.

Combining Old and New for Better Understanding and Predicting Dyslexia.

Despite decades of research, it has been difficult to achieve consensus on a definition of common learning disabilities such as dyslexia. This lack of consensus represents a fundamental problem for the field. Our approach to addressing this issue is to use model-based meta-analyses and Bayesian models with informative priors to combine the results of a large number of studies for the purpose of yielding a more stable and well-supported conceptualization of reading disability. A prerequisite to implementing these models is establishing informative priors for dyslexia. We illustrate a new approach for doing so based on the known distribution of the difference between correlated variables, and use this distribution to determine the proportion of poor readers whose poor reading is unexpected (i.e., likely to be due to dyslexia) as opposed to expected.

The Prevalence of Dyslexia: A New Approach to Its Estimation.

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.