Construct Identification in the Neuropsychological Battery: What Are We Measuring?
Construct Identification in the Neuropsychological Battery: What Are We Measuring?
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DOI：
10.1037/neu0000832
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发表时间：
2022-06-23
期刊：
影响因子：
2.4
通讯作者：
Shih, Stone
中科院分区：
文献类型：
--
作者：
Bilder, Robert M.;Widaman, Keith F.;Shih, Stone
目的：神经心理学数据协调的主要障碍包括缺乏共识的结构和测试是最重要的和不变的健康和临床人群。这项研究使用来自国家神经心理学网络（NNN）的数据解决了这些挑战。方法：数据来自5,000名NNN参与者和Pearson标准化样本。分析包括来自四种工具的变量：韦氏成人智力量表，第4版（WAIS-IV）;韦氏记忆量表，第4版（WMS-IV）;加州言语学习测试，第3版（CVLT 3）;和Delis-Kaplan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，并检查样本之间的拟合统计和测量不变性。我们研究了因素得分与人口统计学和临床特征的关系。结果：对于每种仪器，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是标准化样本中的最佳拟合模型，包括任务特定因素。NNN数据的分析提示规范的一个消除熟悉因素的WMS-IV和抑制开关的D-KEFS的因素。分析显示，样本之间存在强到严格的因子不变性，因子均值和方差存在预期差异。在NNN中，与标准化样本相比，熟悉度因子与年龄的相关性更强。结论：来自健康人群的因素模型通常适合患者。NNN数据有助于识别新的抑制-熟悉和抑制-转换因子，这些因子在样本中也是不变的，可能在临床上有用。研究结果支持努力确定基于证据和最佳有效的测量神经心理学结构，是有效的跨群体。
Objective: 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). Method: 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. Results: 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. Conclusions: 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.