School of Psychological Science, University of Western Australia, Perth, Western Australia.
School of Psychology and Exercise Science, Murdoch University, Perth, Western Australia.
Arch Clin Neuropsychol. 2019 Nov 27;34(8):1356-1366. doi: 10.1093/arclin/acy102.
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.
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.
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.
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.
使用回归技术,提供针对年龄在 60 岁及以上的老年人(60-93 岁)的重复认知评估测试(RBANS)A 型的最新常模数据,并针对教育、年龄和性别进行校正。
参与者(年龄 60-93 岁;N=415)通过西澳大利亚大学的健康老龄化研究计划(HARP)招募,并作为更广泛的神经心理测试组合的一部分完成了 RBANS A 型。使用基于回归的技术而不是基于均值的方法生成常模数据。这种方法允许使用连续数据控制人口统计学变量。为了制定常模,对数据进行了以下评估:(1)正态性;(2)每个子测试分数与年龄、教育程度和性别之间的关系;(3)年龄、教育程度和性别对子测试分数的影响;(4)将残差分数转换为百分位分布。
三个样本之间存在差异,其中一些差异较小,可能不代表临床有意义的差异。年龄较小、受教育年限较长和女性性别与大多数子测试的得分较高有关。使用未标准化的残差分数生成了频率分布、均值和标准差,以消除年龄、教育程度和性别的影响。
这些常模数据通过使用基于回归的技术生成常模,提供百分位数以及均值和标准差,校正性别影响,并提供免费使用的 Excel 宏来计算百分位数,扩展了过去的工作。