Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-26T05:10:51.110Z Has data issue: false hasContentIssue false
  • English
  • Français

Impairment versus deficiency in neuropsychological assessment: Implications for ecological validity

Published online by Cambridge University Press:  01 January 2009

NOAH D. SILVERBERG  and
SCOTT R. MILLIS
NOAH D. SILVERBERG*
Affiliation:
G.F. Strong Rehab Centre, Vancouver, British Columbia, Canada
SCOTT R. MILLIS
Affiliation:
Department of Physical Medicine and Rehabilitation, Wayne State University School of Medicine, Detroit, Michigan
*
*Correspondence and reprint requests to: Noah Silverberg, G.F. Strong Rehab Centre, 4255 Laurel Street, Vancouver, British Columbia V5Z 2G9. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Neuropsychological test interpretation has relied on pre- and postmorbid comparisons, as exemplified by the use of demographically adjusted normative data. We argue that, when the assessment goal is to predict real-world functioning, this interpretive method should be supplemented by “absolute” scores. Such scores are derived from comparisons with the general healthy adult population (i.e., demographically unadjusted normative data) and reflect examinees’ current ability, that is, the interaction between premorbid and injury/disease-related factors. In support of this view, we found that substantial discrepancies between demographically adjusted and absolute scores were common in a traumatic brain injury sample, especially in participants with certain demographic profiles. Absolute scores predicted selected measures of functional outcome better than demographically adjusted scores and also classified participants’ functional status more accurately, to the extent that these scores diverged. In conclusion, the ecological validity of neuropsychological tests may be improved by the consideration of absolute scores. (JINS, 2009, 15, 94–102.)

Keywords

Neuropsychological testsPsychometricsActivities of daily livingWorkBrain injuryChronicCognition disorders
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 15 , Issue 1 , January 2009 , pp. 94 - 102
Copyright
Copyright © INS 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

American Academy of Clinical Neuropsychology Board of Directors. (2007). American Academy of Clinical Neuropsychology (AACN) practice guidelines for neuropsychological assessment and consultation. The Clinical Neuropsychologist, 21, 209–231.CrossRefGoogle Scholar
Boake, C. (1996). Supervision Rating Scale: A measure of functional outcome from brain injury. Archives of Physical Medicine and Rehabilitation, 77, 765–772.CrossRefGoogle Scholar
Burgess, P.W., Alderman, N., Forbes, C., Costello, A., Coates, L.M., Dawson, D.R., Anderson, N.D., Gilbert, S.J., Dumontheil, I., & Channon, S. (2006). The case for the development and use of “ecologically valid” measures of executive function in experimental and clinical neuropsychology. Journal of the International Neuropsychological Society; JINS, 12, 194–209.CrossRefGoogle ScholarPubMed
Chaytor, N. & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology Review, 13, 181–197.CrossRefGoogle ScholarPubMed
Chaytor, N., Scmitter-Edgecombe, M., & Burr, R. (2006). Improving the ecological validity of executive functioning assessment. Archives of Clinical Neuropsychology, 21, 217–227.CrossRefGoogle ScholarPubMed
Crawford, J.R., Garthwaite, P.H., & Gault, C.B. (2007). Estimating the percentage of the population with abnormally low scores (or abnormally large score differences) on standardized neuropsychological test batteries: A generic method with applications. Neuropsychology, 21, 419–430.CrossRefGoogle ScholarPubMed
Crowe, S.F., Mahony, K., & Jackson, M. (2004). Predicting competency in automated machine use in an acquired brain injury population using neuropsychological measures. Archives of Clinical Neuropsychology, 19, 673–691.CrossRefGoogle Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test-Second Edition: Adult Version. San Antonio, TX: The Psychological Corporation.Google Scholar
Dijkers, M. & Greenwald, G. (2007). Functional assessment in TBI rehabilitation. In Zasler, N.D., Katz, D.I., & Zafonte, R.D. (Eds.), Brain Injury Medicine: Principles and Practice. New York: Demos.Google Scholar
Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K., Belleville, S.,Brodaty, H., Bennet, D., Chertkow, H., Cummings, J.L., de Leon, M., Feldman, H., Ganguli, M., Hampel, H., Schetlens, P., Tierney, M.C., Whitehouse, P., Windblad, B., & International Psychogeriatric Association Expert Conference on mild cognitive impairment. (2006). Mild cognitive impairment. Lancet, 367, 1262–1270.CrossRefGoogle ScholarPubMed
Guilmette, T.J., Hagan, L.D., & Giuliano, A.J. (2008). Assigning qualitative descriptions to test scores in neuropsychology: Forensic implications. The Clinical Neuropsychologist, 22, 122–139.CrossRefGoogle ScholarPubMed
Hallam, B.J., Silverberg, N.D., Lamarre, A.K., Mackenzie, I.R., & Feldman, H.H. (2008). Clinical presentation of prodromal frontotemporal dementia. American Journal of Alzheimer’s Disease and Other Dementias, 22, 456–467.CrossRefGoogle Scholar
Hammond, F.M., Grattan, K.D., Sasser, H., Corrigan, J.D., Rosenthal, M., Bushnick, T., & Shull, W. (2004). Five years after traumatic brain injury: A study of individual outcomes and predictors of change in function. NeuroRehabilitation, 19, 25–35.CrossRefGoogle ScholarPubMed
Hardin, J.W. & Hilbe, J. (2007). Generalized Linear Model and Extensions. College Station, TX: State PressGoogle Scholar
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.C., & Curtiss, G. (1993). Wisconsin Card Sorting Test Manual: Revised and Expanded. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Heaton, R.K., Miller, S., Taylor, M.J., & Grant, I. (2004). Revised Comprehensive Norms for an Expanded Halstead-Reitan Battery: Demographically Adjusted Neuropsychological Norms for African American and Caucasian Adults. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Ivnik, I.J. (2005). Normative psychology: A professional obligation? The Clinical Neuropsychologist, 19, 159–161.Google ScholarPubMed
Lange, R.T., Chelune, G.J., & Tulsky, D.S. (2006). Development of WAIS-III General Ability Index Minus WMS-III memory discrepancy scores. The Clinical Neuropsychologist, 20, 382–395.CrossRefGoogle ScholarPubMed
Lange, R.T., Schoenberg, M.R., Chelune, G.J., Scott, J.G., & Adams, R.L. (2005). Developmental of the WAIS-III General Ability Index Estimate (GAI-E). The Clinical Neuropsychologist, 19, 73–86.CrossRefGoogle Scholar
LeBlanc, J.M., Hayden, M.E., & Paulman, R.G. (2000). A comparison of neuropsychological and situational assessment for predicting employability after closed head injury. Journal of Head Trauma Rehabilitation, 15(4), 1022–1040.Google Scholar
Levin, H.S., Boake, C., Song, J., McCauley, S., Contant, C., Diaz-Marchan, P., Brundage, S., Goodman, H., & Kotria, K.J. (2001). Validity and sensitivity to change of the Extended Glasgow Outcome Scale in mild to moderate traumatic brain injury. Journal of Neurotrauma, 18, 575–584.CrossRefGoogle ScholarPubMed
Lewis, R. & Rennick, P.M. (1979). Manual for the Repeatable Cognitive-Pereceptual-Motor Battery. Grosse Point, MI: Axon.Google Scholar
Lezak, M.D., Howieson, D., & Loring, D. (2004). Neuropsychological Assessment (4th ed.). New York: Oxford University Press.Google Scholar
Manly, J.J. & Echemendia, R.J. (2007). Race-specific norms: Using the model of hypertension to understand issues of race, culture, and education in neuropsychology. Archives of Clinical Neuropsychology, 22, 319–325.CrossRefGoogle ScholarPubMed
Miller, L.S. & Rohling, M.L. (2001). A statistical interpretive method for neuropsychological test data. Neuropsychology Review, 11, 143–169.CrossRefGoogle ScholarPubMed
Mitrushina, M., Boone, K.B., Razani, J., & D’Elia, L.F. (2005). Handbook of Normative Data for Neuropsychological Assessment (2nd ed.). New York: Oxford University Press.Google Scholar
Peterson, D.B. (2005). International classification of functioning, disability, and health: An introduction for rehabilitation psychologists. Rehabilitation Psychology, 50, 105–112.CrossRefGoogle Scholar
Rappaport, M., Hall, K.M., Hopkins, K., Belleza, T., & Cope, D.N. (1982). Disability rating scale for severe head trauma: Coma to community. Archives of Physical Medicine and Rehabilitation, 63, 118–123.Google ScholarPubMed
Ready, R.E., Stierman, L., & Paulsen, J.S. (2001). Ecological validity of neuropsychological and personality measures of executive functions. The Clinical Neuropsychologist, 15, 314–323.CrossRefGoogle ScholarPubMed
Reitan, R. & Wolfson, D. (1985). The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. Tucson, AZ: Neuropsychology Press.Google Scholar
Reitan, R.M. & Wolfson, D. (2005). The effect of age and education transformations and neuropsychological test scores of persons with diffuse or bilateral brain damage. Applied Neuropsychology, 12, 181–189.CrossRefGoogle ScholarPubMed
Sbordone, R.J. & Guilmette, T.J. (1999). Ecological validity: Prediction of everyday and vocational functioning from neuropsychological test data. In Sweet, J.J. (Ed.), Forensic Neuropsychology: Fundamentals and Practice. Lisse: Swets and Zeitlinger.Google Scholar
Schretlen, D.J., Buffington, A.L., Meyer, S.M., & Pearlson, G.D. (2005). The use of word-reading to estimate “premorbid” ability in cognitive domains other than intelligence. Journal of the International Neuropsychological Society, 11, 784–787.CrossRefGoogle ScholarPubMed
Sherrill-Pattison, S., Donders, J., & Thompson, E. (2000). Influence of demographic variables on neuropsychological test performance after traumatic brain injury. The Clinical Neuropsychologist, 14, 496–503.CrossRefGoogle ScholarPubMed
Spooner, D.M. & Pachana, N.A. (2006). Ecological validity in neuropsychological assessment: A case for greater consideration in research with neurologically intact populations. Archives of Clinical Neuropsychology, 21, 327–337.CrossRefGoogle ScholarPubMed
Strauss, E., Spreen, O., & Elisabeth, S. (2006). Compendium of Neuropsychological Tests: Administration, Norms, and Commentary (3rd ed.). New York: Oxford University Press.Google Scholar
Steinberg, B. & Bieliauskas, L. (2005). Introduction to the special edition: IQ-based MOANS norms for multiple neuropsychological instruments. The Clinical Neuropsychologist, 19, 277–279.CrossRefGoogle Scholar
Taylor, M.J. & Heaton, R.K. (2001). Sensitivity and specificity of WAIS-III/WMS-III demographically corrected factor scores in neuropsychological assessment. Journal of the International Neuropsychological Society, 7, 867–874.CrossRefGoogle ScholarPubMed
Testa, A. (2007). Regression-based norms: Historical development and current applications. Paper presented at the Thirty-Fifth Annual Meeting of the International Neuropsychological Society, Portland, Oregon.Google Scholar
Tuokko, H., Garrett, D.D., McDowell, I., Silverberg, N., & Kristjansson, B. (2003). Cognitive decline in high-functioning older adults: Reserve or ascertainment bias? Aging and Mental Health, 7, 259–270.CrossRefGoogle ScholarPubMed
Tuokko, H. & Woodward, T.S. (1996). Development and validation of a demographic correction system for neuropsychological measures used in the Canadian Study of Health and Aging. Journal of Clinical and Experimental Neuropsychology, 18, 479–616.CrossRefGoogle ScholarPubMed
van Baalen, B., Odding, E., van Woensel, M.P., & van Kessel, M.A. (2006). Reliability and sensitivity to change of measurement instruments used in a traumatic brain injury population. Clinical Rehabilitation, 20, 686–700.CrossRefGoogle Scholar
Walker, N., Mellick, D., Brooks, C.A., & Whiteneck, G.G. (2003). Measurement participation across impairment groups using the Craig Handicap Assessment Reporting Technique. American Journal of Physical Medicine and Rehabilitation, 82, 936–941.CrossRefGoogle ScholarPubMed
Whiteneck, G., Fougeyrolles, P., & Gerhart, K.A. (1997). Elaborating the model of disablement. In Fuhrer, M. (Ed.), Assessing Medical Rehabilitation Practices: The Promise of Outcomes Research. Baltimore: Brooks Publishing.Google Scholar
Williams, J.M. (1997). The prediction of premorbid memory ability. Archives of Clinical Neuropsychology, 12, 745–756.CrossRefGoogle ScholarPubMed
Wilson, J.T., Pettigrew, L.E., & Teasdale, G.M. (1998). Structured interviews for the Glasgow Outcome Scale and the Extended Glasgow Outcome Scale: Guidelines for their use. Journal of Neurotrauma, 15, 573–585.CrossRefGoogle ScholarPubMed
Yantz, C.L., Gavett, B.E., Lynch, J.K., & McCaffrey, M.J. (2006). Potential for interpretation disparities of Halstead-Reitan neuropsychological battery performances in a litigating sample. Archives of Clinical Neuropsychology, 21, 809–817.CrossRefGoogle Scholar