Jane & Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles.
Graduate School of Education, University of California, Riverside.
Neuropsychology. 2023 May;37(4):351-372. doi: 10.1037/neu0000832. Epub 2022 Jun 23.
Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN).
Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics.
For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample.
Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
神经心理学中数据协调的主要障碍包括缺乏共识，即哪些结构和测试在健康人群和临床人群中最重要且不变。本研究使用国家神经心理学网络（NNN）的数据解决了这些挑战。
数据来自 NNN 的 5000 名参与者和 Pearson 标准化样本。分析包括来自四个工具的变量：韦氏成人智力量表第四版（WAIS-IV）；韦氏记忆量表第四版（WMS-IV）；加利福尼亚语言学习测试第三版（CVLT3）；以及德利斯科尔-卡普兰执行功能系统（D-KEFS）。我们使用验证性因子分析来评估先前工作提出的模型，并检查样本间的拟合统计量和测量不变性。我们还研究了因子分数与人口统计学和临床特征的关系。
对于每个工具，我们确定了四个一阶和一个二阶因子。患者的最佳模型通常与标准化样本中的最佳拟合模型相似，包括特定任务的因素。对 NNN 数据的分析促使我们在 WMS-IV 上指定了一个识别-熟悉因素，在 D-KEFS 上指定了一个抑制-转换因素。分析表明，样本间的因子结构具有很强的不变性，因子均值和方差存在预期差异。在 NNN 中，识别-熟悉因子与年龄的相关性比在标准化样本中更强。
从健康人群中得出的因子模型通常在患者中拟合良好。NNN 数据有助于识别新的识别-熟悉和抑制-转换因子，这些因子在样本间也是不变的，并且可能具有临床意义。这些发现支持努力识别基于证据的、最佳有效的神经心理结构测量方法，这些方法在各群体中都是有效的。（PsycInfo 数据库记录（c）2023 APA，保留所有权利）。
