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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.