Methodology for the generation of normative data for the U.S. adult Spanish-speaking population: A Bayesian approach.

Abstract

Hispanics are the largest growing ethnic minority group in the U.S. Despite significant progress in providing norms for this population, updated normative data are essential. To present the methodology for a study generating normative neuropsychological test data for Spanish-speaking adults living in the U.S. using Bayesian inference as a novel approach. The sample consisted of 253 healthy adults from eight U.S. regions, with individuals originating from a diverse array of Latin American countries. To participate, individuals must have met the following criteria: were between 18 and 80 years of age, had lived in the U.S. for at least 1 year, self-identified Spanish as their dominant language, had at least one year of formal education, were able to read and write in Spanish at the time of evaluation, scored≥23 on the Mini-Mental State Examination, <10 on the Patient Health Questionnaire- 9, and <10 on the Generalized Anxiety Disorder scale. Participants completed 12 neuropsychological tests. Reliability statistics and norms were calculated for all tests. This is the first normative study for Spanish-speaking adults in the U.S. that uses Bayesian linear or generalized linear regression models for generating norms in neuropsychology, implementing sociocultural measures as possible covariates.