Combining Old and New for Better Understanding and Predicting Dyslexia

Abstract

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.

Figure 1.

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

Figure 2.

Scatterplot of listening comprehension and reading comprehension. Points to the left of the vertical line represent poor reading (i.e., at or below the 20^th %-ile). Points above the slanted line represent listening comprehension better than reading comprehension (i.e., at or above the 80^th %-ile).