Division of Neuropsychology, Department of Neurology, Baylor Scott and White Health, Temple, TX, USA.
Michael E. DeBakey VA Medical Center, Mental Health Care Line, Houston, TX, USA.
J Int Neuropsychol Soc. 2020 Jul;26(6):576-586. doi: 10.1017/S135561772000003X. Epub 2020 Feb 17.
The goals of this study were to (1) specify the factor structure of the Uniform Dataset 3.0 neuropsychological battery (UDS3NB) in cognitively unimpaired older adults, (2) establish measurement invariance for this model, and (3) create a normative calculator for factor scores.
Data from 2520 cognitively intact older adults were submitted to confirmatory factor analyses and invariance testing across sex, age, and education. Additionally, a subsample of this dataset was used to examine invariance over time using 1-year follow-up data (n = 1061). With the establishment of metric invariance of the UDS3NB measures, factor scores could be extracted uniformly for the entire normative sample. Finally, a calculator was created for deriving demographically adjusted factor scores.
A higher order model of cognition yielded the best fit to the data χ2(47) = 385.18, p < .001, comparative fit index = .962, Tucker-Lewis Index = .947, root mean square error of approximation = .054, and standardized root mean residual = .036. This model included a higher order general cognitive abilities factor, as well as lower order processing speed/executive, visual, attention, language, and memory factors. Age, sex, and education were significantly associated with factor score performance, evidencing a need for demographic correction when interpreting factor scores. A user-friendly Excel calculator was created to accomplish this goal and is available in the online supplementary materials.
The UDS3NB is best characterized by a higher order factor structure. Factor scores demonstrate at least metric invariance across time and demographic groups. Methods for calculating these factors scores are provided.
本研究旨在:(1)明确认知正常老年人统一数据集 3.0 神经心理学成套测验(UDS3NB)的因子结构;(2)验证该模型的测量不变性;(3)为因子得分创建一个常模计算器。
对 2520 名认知正常的老年人的数据进行验证性因子分析和跨性别、年龄和教育的不变性检验。此外,使用该数据集的一个子样本(n = 1061)通过 1 年的随访数据来检验时间上的不变性。在 UDS3NB 测量的度量不变性建立后,可以为整个常模样本统一提取因子得分。最后,创建了一个计算器来计算人口统计学调整后的因子得分。
认知的高阶模型为数据提供了最佳拟合 χ2(47) = 385.18,p <.001,比较拟合指数=.962,塔克-刘易斯指数=.947,均方根误差逼近值=.054,标准化均方根残差=.036。该模型包括高阶一般认知能力因素,以及低阶处理速度/执行、视觉、注意力、语言和记忆因素。年龄、性别和教育与因子得分表现显著相关,表明在解释因子得分时需要进行人口统计学校正。创建了一个用户友好的 Excel 计算器来实现这一目标,并在在线补充材料中提供。
UDS3NB 最好用高阶因子结构来描述。因子得分在时间和人口统计学群体之间至少具有度量不变性。提供了计算这些因子得分的方法。