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

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Conclusion: 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.