Comparative Utility of Barona Formulae, Wtar Demographic Algorithms, and WRAT-3 Reading for Estimating Premorbid Ability in a Diverse Research Sample

This is the authors' abstract. We don't add key points for this paper.

Various Barona formulae, a WTAR algorithm based on demographic data, and WRAT-3 oral reading methods of estimating premorbid ability were compared in a diverse research sample of 119 subjects. These methods were correlated with one another and with a modified version of the Raven Standard Progressive Matrices. Descriptive data are provided to illustrate advantages and disadvantages of various methods of estimating premorbid ability when no formal intellectual testing is available. While predicting premorbid ability for individual subjects involves varying degrees of error, we found that the revised Barona formula was superior to the original formula for subjects at the upper end of ability level. When researchers have screened out learning disability and have subject samples with few individuals likely to be of superior premorbid intelligence, oral reading scores are a reasonable measure of premorbid ability. Otherwise, researchers are advised to use both demographic and oral reading methods to estimate premorbid ability.

May support revised Barona for high-ability premorbid estimates; hypothesis-generating for oral reading utility pending controlled validation.