Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Normative Data for Older Adults

Olaithe, M., Weinborn, M., Lowndes, T., Ng, A., Hodgson, E., Fine, L., Parker, D., Pushpanathan, M., Bayliss, D., Anderson, M. and Bucks, R.S. (2019) Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Normative Data for Older Adults. Archives of Clinical Neuropsychology, 34 (8). pp. 1356-1366.

Link to Published Version: https://doi.org/10.1093/arclin/acy102
*Subscription may be required

Abstract

Objective
Provide updated older adult (ages 60+) normative data for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Form A, using regression techniques, and corrected for education, age, and gender.

Method
Participants (aged 60–93 years; N = 415) were recruited through the Healthy Ageing Research Program (HARP), University of Western Australia, and completed Form A of the RBANS as part of a wider neuropsychological test battery. Regression-based techniques were used to generate normative data rather than means-based methods. This methodology allows for the control of demographic variables using continuous data. To develop norms, the data were assessed for: (1) normality; (2) associations between each subtest score and age, education, and gender; (3) the effect of age, education, and gender on subtest scores; and (4) residual scores which were converted to percentile distributions.

Results
Differences were noted between the three samples, some of which were small and may not represent a clinically meaningful difference. Younger age, more years of education, and female gender were associated with better scores on most subtests. Frequency distributions, means, and standard deviations were produced using unstandardized residual scores to remove the effects of age, education, and gender.

Conclusions
These normative data expand upon past work by using regression-based techniques to generate norms, presenting percentiles, as well as means and standard deviations, correcting for the effect of gender, and providing a free-to-use Excel macro to calculate percentiles.

Item Type: Journal Article
Murdoch Affiliation: School of Psychology and Exercise Science
Publisher: Oxford Journals
Copyright: © 2020 Oxford University Press
URI: http://researchrepository.murdoch.edu.au/id/eprint/54068
Item Control Page Item Control Page