Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status

Initial learning and learning slope are often acknowledged as important qualitative aspects of learning, but the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) contains discrete indices for neither. The traditional method of calculating learning slope involves a difference score between the last trial and first trial, which is referred to as raw learning score (RLS). However, this method does not account for initial Trial One performance and produces a ceiling effect that penalizes efficient first learners. We propose an alternative method of calculating learning score that accounts for initial learning performance, called learning ratio (LR), and we compared the psychometric and predictive properties of these methods. Performances from the List Learning and Story Memory subtests were used to create the indices, and composite learning scores were calculated by combining List Learning and Story Memory. The sample included 289 military veterans (mean age = 65.9 [12.6], education = 13.3 [2.4]), most of whom were male, undergoing neuropsychological assessments that included the RBANS. Results indicated that LR demonstrated superior correlations with criterion measures of memory when compared with RLS, and the LR composite score better discriminated between those with and without a neurocognitive diagnosis, AUC = 0.81 (0.76-0.87), than the RLS composite, AUC = 0.70 (0.64-0.76). We concluded that scores from the RBANS can be computed for initial learning and learning slope and that the LR method of calculating learning is superior to RLS in this older veteran sample.