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Examination of the Factor Structure of a Global Cognitive Function Battery across Race and Time

Published online by Cambridge University Press:  13 November 2015

Lisa L. Barnes ,
Futoshi Yumoto ,
Ana Capuano ,
Robert S. Wilson ,
David A. Bennett  and
Rochelle E. Tractenberg
Lisa L. Barnes*
Affiliation:
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
Futoshi Yumoto
Affiliation:
Collaborative for Research on Outcomes and Metrics, USA Merkle, Columbia, Maryland
Ana Capuano
Affiliation:
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
Robert S. Wilson
Affiliation:
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois
David A. Bennett
Affiliation:
Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois
Rochelle E. Tractenberg
Affiliation:
Collaborative for Research on Outcomes and Metrics, USA Departments of Neurology and Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, DC
*
Correspondence and reprint requests to: Lisa L Barnes, Rush Alzheimer’s Disease Center, Rush University Medical Center, Armour Academic Center, 600 S. Paulina, Suite 1038, Chicago, IL 60612. E-mail: [email protected]
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Abstract

Older African Americans tend to perform more poorly on cognitive function tests than older Whites. One possible explanation for their poorer performance is that the tests used to assess cognition may not reflect the same construct in African Americans and Whites. Therefore, we tested measurement invariance, by race and over time, of a structured 18-test cognitive battery used in three epidemiologic cohort studies of diverse older adults. Multi-group confirmatory factor analyses were carried out with full-information maximum likelihood estimation in all models to capture as much information as was present in the observed data. Four different aspects of the data were fit to each model: comparative fit index (CFI), standardized root mean square residuals (SRMR), root mean square error of approximation (RMSEA), and model $$\chi ^{2} $$ . We found that the most constrained model fit the data well (CFI=0.950; SRMR=0.051; RMSEA=0.057 (90% confidence interval: 0.056, 0.059); the model $$\chi ^{2} $$ =4600.68 on 862 df), supporting the characterization of this model of cognitive test scores as invariant over time and racial group. These results support the conclusion that the cognitive test battery used in the three studies is invariant across race and time and can be used to assess cognition among African Americans and Whites in longitudinal studies. Furthermore, the lower performance of African Americans on these tests is not due to bias in the tests themselves but rather likely reflect differences in social and environmental experiences over the life course. (JINS, 2016, 22, 66–75)

Keywords

Measurement invarianceAfrican AmericanLongitudinalEpidemiologyCognitionCohort study
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Issue 1 , January 2016 , pp. 66 - 75
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Aiken-Morgan, A.T., Gamaldo, A.A., Sims, R.C., Allaire, J.C., & Whitfield, K.E. (2014). Education desegregation and cognitive change in African American older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 348–356.CrossRefGoogle ScholarPubMed
Albert, M., Smith, L.A., Scherr, P.A., Taylor, J.O., Evans, D.A., & Funkenstein, H.H. (1991). Use of brief cognitive tests to identify individuals in the community with clinically diagnosed Alzheimer’s disease. International Journal of Neuroscience, 57, 167–178.CrossRefGoogle ScholarPubMed
Allison, P.D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545–557.Google Scholar
Arnold, B.R., Montgomery, G.T., Castaneda, I., & Longoria, R. (1994). Acculturation and performance of Hispanics on selected Halstead-Reitan neuropsychological tests. Assessment, 1, 239–248.Google Scholar
Barnes, L.L., Lewis, T.T., Begeny, C.T., Yu, L., Bennett, D.A., & Wilson, R.S. (2012). Perceived discrimination and cognition in older African Americans. Journal of the International Neuropsychological Society, 18, 856–865.Google Scholar
Barnes, L.L., Shah, R.C., Aggarwal, N.T., Bennett, D.A., & Schneider, J.A. (2012). The Minority Aging Research Study: Ongoing efforts to obtain brain donation in African Americans without dementia. Current Alzheimer Research, 9, 734–745.Google Scholar
Bennett, D.A., Schneider, J.A., Arvanitakis, Z., & Wilson, R.S. (2012). Overview and findings from the Religious Orders Study. Current Alzheimer Research, 9, 628–645.Google Scholar
Bennett, D.A., Schneider, J.A., Buchman, A.S., Barnes, L.L., Boyle, P.A., & Wilson, R.S. (2012). Overview and findings from the rush Memory and Aging Project. Current Alzheimer Research, 9, 646–663.CrossRefGoogle ScholarPubMed
Benton, A.L., Sivan, A.B., Hamsher, K.D., Varney, N.R., & Spreen, O. (1994). Contributions to neuropsychological assessment (2nd ed.). New York: Oxford University Press.Google Scholar
Blankson, A.N., & McArdle, J.J. (2013). Measurement invariances of cognitive abilities across ethnicity, gender, and time among older Americans. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 70, 386–397.Google Scholar
Bollen, K.A., & Curran, P.J. (2006). Latent curve models: A structural equation perspective. New York, NY: Wiley.Google Scholar
Bowden, S.C., Weiss, L.G., Holdnack, J.A., & Lloyd, D. (2006). Age-related invariance of abilities measured with the Wechsler Adult Intelligence Scale–III. Psychological Assessment, 18, 334–339.Google Scholar
Brewster, P.W.H., Melrose, R.J., Marquine, M.J., Johnson, J.K., Napoles, A., MacKay-Brandt, A., & Mungas, D. (2014). Life experience and demographic influences on cognitive function in older adults. Neuropsychology, 28, 846–858.Google Scholar
Brickman, A.M., Cabo, R., & Manly, J.J. (2006). Ethical issues in cross-cultural neuropsychology. Applied Neuropsychology, 13, 91–100.Google Scholar
Burnham, K.P., & Anderson, D.R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York, NY: Springer.Google Scholar
Byrne, B.M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and programming (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Byrne, B.M. (2011). Structural equation modeling with Mplus: Basic concepts, applications, and programming. New York, NY: Routledge.Google Scholar
Census Bureau. Retrieved from www.census.gov/population/www/socdemo.race.html Google Scholar
Chen, F.F. (2007). Sensitivity of goodness of fit indices to lack of measurement invariance. Structural Equation Modeling, 14, 464–504.CrossRefGoogle Scholar
Chen, F.F., Sousa, K.H., & West, S.G. (2005). Testing measurement invariance of second-order factor models. Structural Equation Modeling, 12, 471–492.CrossRefGoogle Scholar
Cooper, J.A., & Sagar, H.J. (1993). Incidental and intentional recall in Parkinson’s disease: An account based on diminished attentional resources. Journal of Clinical and Experimental Neuropsychology, 15, 713–731.CrossRefGoogle ScholarPubMed
Crowe, M., Clay, O.J., Martin, R.C., Howard, V.J., Wadley, V.G., Sawyer, P., & Allman, R.M. (2013). Indicators of childhood quality of education in relation to cognitive function in older adulthood. The Journals of Gerontology: Series A: Biological Sciences and Medical Sciences, 68, 198–204.CrossRefGoogle ScholarPubMed
Crowe, M., Sartori, A., Clay, O.J., Wadley, V.G., Andel, R., Wang, H.X., & Allman, R.M. (2010). Diabetes and cognitive decline: Investigating the potential influence of factors related to health disparities. Journal of Aging and Health, 22, 292–306.CrossRefGoogle ScholarPubMed
Early, D.R., Widaman, K.F., Harvey, D., Beckett, L., Park, L.Q., Farias, S.T., & Mungas, D. (2013). Demographic predictors of cognitive change in ethnically diverse older persons. Psychology & Aging, 28, 633–645.Google Scholar
Ekstrom, R.B., French, J.W., Harman, H.H., & Kermen, D. (1976). Manual for kit of factor-referenced cognitive tests. Princeton, NJ: Educational Testing Service.Google Scholar
Hayden, K.M., Jones, R.N., Zimmer, C., Plassman, B.L., Browndyke, J.N., Pieper, C., & Welsh-Bohmer, K.A. (2011). Factor structure of the National Alzheimer’s Coordinating Centers uniform dataset neuropsychological battery: An evaluation of invariance between and within groups over time. Alzheimer Disease and Associated Disorders, 25, 128–137.Google Scholar
Hertzog, C., & Schaie, K.W. (1986). Stability and change in adult intelligence: Analysis of longitudinal covariance structures. Psychology and Aging, 1, 159–171.CrossRefGoogle ScholarPubMed
Horn, J.L., & McArdle, J.J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18, 117–144.CrossRefGoogle ScholarPubMed
Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.Google Scholar
Jones, R.N. (2003). Racial bias in the assessment of cognitive functioning of older adults. Aging & Mental Health, 7, 83–102.Google Scholar
Kaplan, E.F., Goodglass, H., & Weintraub, S. (1983). The Boston Naming Test. Philadelphia: Lea & Febiger.Google Scholar
Loewenstein, D.A., Arguelles, T., Arguelles, S., & Linn-Fuentes, P. (1994). Potential cultural bias in the neuropsychological assessment of the older adult. Journal of Clinical and Experimental Neuropsychology, 16, 623–629.Google Scholar
Maitland, S.B., Intrieri, R.C., Schaie, K.W., & Willis, S.L. (2000). Gender differences and changes in cognitive abilities across the adult life span. Aging Neuropsychology & Cognition, 7, 32–53.Google Scholar
Manly, J.J. (2005). Advantages and disadvantages of separate norms for African Americans. The Clinical Neuropsychologist, 19, 270–275.Google Scholar
Manly, J.J., Jacobs, D.M., Touradji, P., Small, S.A., & Stern, Y. (2002). Reading level attenuates differences in neuropsychological test performance between African American and White elders. Journal of the International Neuropsychological Society, 8, 341–348.CrossRefGoogle ScholarPubMed
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E.M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34, 939–944.Google Scholar
Meredith, W., & Teresi, J.A. (2006). An essay on measurement and factorial invariance. Medical Care, 44, S69–S77.Google Scholar
Millsap, R.E. (2011). Statistical approaches to measurement invariance. New York, NY: Routledge.Google Scholar
Morris, J.C., Heyman, A., Mohs, R.C., Hughes, J.P., van Belle, G., Fillenbaum, G., & Clark, C. (1989). The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD): I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology, 39, 1159–1165.Google Scholar
Mulaik, S.A. (2010). Foundations of factor analysis (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Mungas, D., Widaman, K.F., Reed, B.R., & Tomaszewski, F.S. (2011). Measurement invariance of neuropsychological tests in diverse older persons. Neuropsychology, 25, 260–269.CrossRefGoogle ScholarPubMed
Muthén, L.K., & Muthén, B.O. (1998-2012). Mplus User’s Guide (6th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Raven, J.C., Court, J.H., & Raven, J. (1992). Manual for Raven’s progressive matrices and vocabulary. Oxford: Oxford Psychologists Press.Google Scholar
Schaie, K.W., Willis, S.L., Jay, G., & Chipuer, H. (1989). Structural invariance of cognitive abilities across the adult life span: A cross-sectional study. Developmental Psychology, 25, 652–662.Google Scholar
Schwartz, B.S., Glass, T.A., Bolla, K.I., Stewart, W.F., Glass, G., Rasmussen, M., & Bandeen-Roche, K. (2004). Disparities in cognitive functioning by race/ethnicity in the Baltimore Memory Study. Environmental Health Perspectives, 112, 314–320.CrossRefGoogle Scholar
Siedlecki, K.L., Honig, L.S., & Stern, Y. (2008). Exploring the structure of a neuropsychological battery across healthy elders and those with questionable dementia and Alzheimer’s disease. Neuropsychology, 22, 400–411.Google Scholar
Siedlecki, K.L., Manly, J.J., Brickman, A.M., Schupf, N., Tang, M.X., & Stern, Y. (2010). Do neuropsychological tests have the same meaning in Spanish speakers as they do in English speakers? Neuropsychology, 24, 402–411.Google Scholar
Silverberg, N.D., Hanks, R.A., & Tompkins, S.C. (2013). Education quality, reading recognition, and racial differences in the neuropsychological outcome from traumatic brain injury. Archives of Clinical Neuropsychology, 28, 485–491.Google Scholar
Sloan, F.A., & Wang, J. (2005). Disparities among older adults in measures of cognitive function by race or ethnicity. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 60, 242–250.Google Scholar
Smith, A. (1982). Symbol Digit Modalities Test Manual - Revised. Los Angeles: Western Psychological Services.Google Scholar
Thames, A.D., Hinkin, C.H., Byrd, D.A., Bilder, R.M., Duff, K.H., Mindt, M.R., & Streiff, V. (2013). Effects of stereotype threat, perceived discrimination, and examiner race on neuropsychological performance: Simple as black and white? Journal of the International Neuropsychological Society, 19, 583–593.Google Scholar
Tractenberg, R.E., Aisen, P.S., Weiner, M.F., Cummings, J.L., & Hancock, G.R. (2006). Independent contributions of neural and ‘higher order’ deficits to symptoms in AD: A latent variable approach. Alzheimer’s & Dementia, 2, 303–313.Google Scholar
Trenerry, M.R., Crosson, B., DeBoe, J., & Leber, W.R. (1989). Stroop Neuropsychological Screening Test Manual. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Tuokko, H.A., Chou, P.H., Bowden, S.C., Simard, M., Ska, B., & Crossley, M. (2009). Partial measurement equivalence of French and English versions of the Canadian Study of Health and Aging neuropsychological battery. Journal of the International Neuropsychological Society, 15, 416–425.Google Scholar
Wechsler, D. (1987). Wechsler Memory Scale-Revised manual. San Antonio, TX: Psychological Corporation.Google Scholar
Wilson, R.S., Barnes, L.L., Krueger, K.R., Hoganson, G., Bienias, J.L., & Bennett, D.A. (2005). Early and late life cognitive activity and cognitive systems in old age. Journal of the International Neuropsychological Society, 11, 400–407.Google Scholar
Wilson, R.S., Beckett, L.A., Barnes, L.L., Schneider, J.A., Bach, J., Evans, D.A., & Bennett, D.A. (2002). Individual differences in rates of change in cognitive abilities of older persons. Psychology and Aging, 17, 179–193.Google Scholar
Wilson, R.S., Fleischman, D.A., Myers, R.A., Bennett, D.A., Bienias, J.L., Gilley, D.W., & Evans, D.A. (2004). Premorbid proneness to distress and episodic memory impairment in Alzheimer’s disease. Journal of Neurology, Neurosurgery, & Psychiatry, 75, 191–195.Google Scholar