A Bayesian Approach to the Identification of Children with Learning Disabilities

Abstract

It is suggested that identification of learning disabilities be divided into two stages, screening and clinical diagnosis, to permit more effective use of clinical resources. The sequential revision of the probability that a child has learning disability based on data about the child and implemented by Bayes' rule is proposed as an efficient screening method. Forty component disabilities, potential data for screening, were selected and classified. For want of objective data, subjective estimates of the relatedness of these disabilities and their statistical prevalence among children with and without learning disability (quantities needed for the Bayesian revision) were obtained from specialists in this field. Some of the disabilities were found to be potentially highly diagnostic and relatively independent and could be used for identification.