For example, Heaton and colleagues (2004) provided normative data for an expanded Halstead-Reitan Neuropsychological Battery that corrects for age, education, and gender using stepwise linear regression. As such, demographic corrections are routinely applied to normative data for various assessment instruments ( Lezak, Howieson, Bigler, & Tranel, 2012 Mitrushina, Boone, Razani, & D'Elia, 2005 Strauss, Sherman, & Spreen, 2006). Norms, normative studies, assessment, elderly, geriatrics, aging IntroductionÄemographic variables, such as age, education, and gender, have long been noted to alter scores on a variety of neuropsychological measures ( Heaton, Miller, Taylor, & Grant, 2004 Vanderploeg, Axelrod, Sherer, Scott, & Adams, 1995). Although some differences were present between the lookup and regression-based norms, it is expected that these current results will present more accurate demographic corrections that allow clinicians and researchers to better interpret individual performances on the RBANS. Results indicated that ∼11% of the variance of Index scores was accounted for by these demographic variables, and 13% of variance in subtest scores.
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Using data from the prior studies, linear regression was used to generate such formulae in the Indexes and subtests of the RBANS. However, regression-based normative formulae that simultaneously correct for all demographic variables may be more sensitive for detecting late life cognitive decline. Prior studies presented lookup tables for RBANS normative data based on age, gender, education, and race using a group of 718 community-dwelling older adults.
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The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has become a popular cognitive screening instrument, particularly in elderly patients.