title: Demographic corrections appear to compromise classification accuracy for severely skewed cognitive tests.
abstract: Demographic corrections for cognitive tests should improve classification accuracy by reducing age or education biases, but empirical support has been equivocal. Using a simulation procedure, we show that creating moderate or extreme skewness in cognitive tests compromises the classification accuracy of demographic corrections, findings that appear replicated within clinical data for the few neuropsychological test scores with an extreme degree of skew. For most neuropsychological tests, the dementia classification accuracy of raw and demographically corrected scores was equivalent. These findings suggest that the dementia classification accuracy of demographic corrections is robust to slight degrees of skew (i.e., skewness <1.5).
authors.affiliation: Department of Psychology, University of Saskatchewan, Saskatoon, SK, Canada. megan.oconnell@usask.ca
authors.forename: Megan E
authors.initials: ME
authors.lastname: O'Connell
authors.forename: Holly
authors.initials: H
authors.lastname: Tuokko
authors.forename: Helena
authors.initials: H
authors.lastname: Kadlec
mesh_terms: D000375:Aging; D019540:Area Under Curve; D002170:Canada / Q000453:epidemiology; D003072:Cognition Disorders / Q000145:classification* / Q000453:epidemiology; D003198:Computer Simulation; D003704:Dementia / Q000145:classification* / Q000453:epidemiology; D003710:Demography; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D012044:Regression Analysis; D012720:Severity of Illness Index