Salthouse T A, Becker J T
School of Psychology, Georgia Institute of Technology, Atlanta 30332-0170, USA.
Neuropsychology. 1998 Apr;12(2):242-52. doi: 10.1037//0894-4105.12.2.242.
A new analytical procedure, single common factor analysis, was carried out on the data from a relatively large sample of normals (n = 101) and patients with Alzheimer's disease (AD; n = 180) to examine the extent to which there were independent effects of disease status on different neuropsychological variables. This technique uses structural equation methods to determine what all of the variables have in common, and then controls this common factor when examining the relationship between diagnostic group and each individual test variable. To the extent that AD represents the sum of independent breakdowns of different information processing domains, then there should be sets of variables that have weak or nonexistent links to the other variables. However, the results revealed that a large proportion of the AD-related effects on test scores was shared and was not independent of the AD-related effects on other variables.
对来自相对大量的正常样本(n = 101)和阿尔茨海默病(AD;n = 180)患者的数据进行了一种新的分析程序——单一共同因素分析,以检验疾病状态对不同神经心理学变量的独立影响程度。该技术使用结构方程方法来确定所有变量的共同之处,然后在检查诊断组与每个单独测试变量之间的关系时控制这个共同因素。如果AD代表不同信息处理领域独立故障的总和,那么应该存在与其他变量联系薄弱或不存在联系的变量集。然而,结果显示,AD对测试分数的大部分影响是共同的,并且与AD对其他变量的影响并非独立。