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Accounting for estimated IQ in neuropsychological test performance with regression-based techniques

Published online by Cambridge University Press:  01 November 2009

S. MARC TESTA ,
JESSICA M. WINICKI ,
GODFREY D. PEARLSON ,
BARRY GORDON  and
DAVID J. SCHRETLEN
S. MARC TESTA
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
JESSICA M. WINICKI
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
GODFREY D. PEARLSON
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland Olin Neuropsychiatry Research Center, Institute of Living/Hartford Hospital, Hartford, Connecticut Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
BARRY GORDON
Affiliation:
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
DAVID J. SCHRETLEN*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
*
*Correspondence and reprint requests to: David J. Schretlen, Johns Hopkins Hospital, 600 N. Wolfe Street, Meyer 218, Baltimore, MD 21287-7218. E-mail: [email protected]
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Abstract

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1–9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated “premorbid” IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated “premorbid” IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently. (JINS, 2009, 15, 1012–1022.)

Keywords

AdultPsychometricsNeuropsychological testsIntelligenceDiagnostic errorsBiometry
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 15 , Issue 6 , November 2009 , pp. 1012 - 1022
Copyright
Copyright © The International Neuropsychological Society 2009

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