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Factor Structure, Convergent Validity, and Discriminant Validity of the NIH Toolbox Cognitive Health Battery (NIHTB-CHB) in Adults

Published online by Cambridge University Press:  24 June 2014

Dan Mungas ,
Robert Heaton ,
David Tulsky ,
Philip David Zelazo ,
Jerry Slotkin ,
David Blitz ,
Jin-Shei Lai  and
Richard Gershon
Dan Mungas*
Affiliation:
Department of Neurology, University of California, Davis, California
Robert Heaton
Affiliation:
Department of Psychiatry, University of California, San Diego, California
David Tulsky
Affiliation:
Rusk Institute / Department of Rehabilitation Medicine, New York University, New York, New York
Philip David Zelazo
Affiliation:
Institute of Child Development, University of Minnesota, Minneapolis, Minnesota
Jerry Slotkin
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
David Blitz
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Jin-Shei Lai
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Richard Gershon
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
*
Correspondence and reprint requests to: Dan Mungas, Department of Neurology, 4860 Y Street, Suite 3700, Sacramento, CA 95817. E-mail: [email protected]
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Abstract

The objective of this study is to evaluate the construct validity of the NIH Neurobehavioral Toolbox Cognitive Health Battery (NIHTB-CHB) in adults. Confirmatory factor analysis was used to evaluate the dimensional structure underlying the NIHTB-CHB and Gold Standard tests chosen to serve as concurrent validity criteria for the NIHTB-CHB. These results were used to evaluate the convergent and discriminant validity of the NIHTB-CHB in adults ranging from 20 to 85 years of age. Five dimensions were found to explain the correlations among NIHTB-CHB and Gold Standard tests: Vocabulary, Reading, Episodic Memory, Working Memory and Executive Function/Processing Speed. NIHTB-CHB measures and their Gold Standard analogues defined factors in a pattern that broadly supported the convergent and discriminant validity of the NIHTB-CHB tests. This 5-factor structure was found to be invariant across 20–60 year old (N=159) and 65–85 year old (N=109) age groups that were included in the current validity study. Second order Crystallized Abilities (Vocabulary and Reading) and Fluid Abilities (Episodic Memory, Working Memory, Executive/Speed) factors parsimoniously explained correlations among the five first order factors. These results suggest that the NIHTB-CHB will provide both fine-grained and broad characterization of cognition across the adult age span. (JINS, 2014, 20, 1–9)

Keywords

CognitionConfirmatory factor analysisNIH ToolboxAdultsConstruct validityTest development
Type
Special Series
Information
Journal of the International Neuropsychological Society , Volume 20 , Issue 6 , July 2014 , pp. 579 - 587
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

REFERENCES

Albinet, C.T., Boucard, G., Bouquet, C.A., & Audiffren, M. (2012). Processing speed and executive functions in cognitive aging: How to disentangle their mutual relationship? Brain and Cognition, 79(1), 1–11.CrossRefGoogle ScholarPubMed
Benedict, R. (1997). Brief Visuospatial Memory Test-Revised professional manual. Odessa, FL: Psychological Assessment Resources, Inc.Google Scholar
Bentler, P.M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246.Google Scholar
Bentler, P.M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.Google Scholar
Bowden, S.C. (2004). The role of factor analysis in construct validity: Is it a myth? Journal of the International Neuropsychological Society, 10(7), 1018–1019.CrossRefGoogle ScholarPubMed
Bowden, S.C., Carstairs, J.R., & Shores, E.A. (1999). Confirmatory factor analysis of combined Wechsler Adult Intelligence Scale-Revised and Wechsler Memory Scale-Revised scores in a healthy community sample. Psychological Assessment, 11(3), 339–344.Google Scholar
Bowden, S.C., Cook, M.J., Bardenhagen, F.J., Shores, E.A., & Carstairs, J.R. (2004). Measurement invariance of core cognitive abilities in heterogeneous neurological and community samples. Intelligence, 32(4), 363–389.Google Scholar
Browne, M., & Cudek, R. (1993). Alternate ways of assessing model fit. In K. Bollen & J. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks, CA: Sage.Google Scholar
Cheung, G.W., & Rensvold, R.B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255.Google Scholar
Crane, P.K., Narasimhalu, K., Gibbons, L.E., Pedraza, O., Mehta, K.M., Tang, Y., … Mungas, D.M. (2008). Composite scores for executive function items: Demographic heterogeneity and relationships with quantitative magnetic resonance imaging. Journal of the International Neuropsychological Society, 14(5), 746–759.Google Scholar
Delis, D.C., Jacobson, M., Bondi, M.W., Hamilton, J.M., & Salmon, D.P. (2003). The myth of testing construct validity using factor analysis or correlations with normal or mixed clinical populations: Lessons from memory assessment. Journal of the International Neuropsychological Society, 9, 936–946.Google Scholar
Delis, D.C., Kramer, J.H., & Kaplan, E. (2001). The Delis-Kaplan Executive Function System. San Antonio, TX: The Psychological Corporation.Google Scholar
Dunn, L.M., & Dunn, D.M. (2007). Peabody Picture Vocabulary Test-Fourth Edition (PPVT-4). Circle Pines, MN: American Guidance Services.Google Scholar
Gershon, R.C., Wagster, M.V., Hendrie, H.C., Fox, N.A., Cook, K.F., & Nowinski, C.J. (2013). NIH toolbox for assessment of neurological and behavioral function. Neurology, 80(11 Suppl 3), S2–S6.Google Scholar
Gronwall, D.M. (1977). Paced auditory serial-addition task: A measure of recovery from concussion. Perceptual and motor skills, 44, 367–373.Google Scholar
Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G.G., & Curtiss, G. (1993). Wisconsin Card Sorting Test. Professional manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
Larrabee, G.J., Kane, R.L., & Schuck, J.R. (1983). Factor analysis of the WAIS and Wechsler Memory Scale: An analysis of the construct validity of the Wechsler Memory Scale. Journal of Clinical Neuropsychology, 5(2), 159–168.Google Scholar
Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., & Wager, T.D. (2000). The unity and diversity of executive functions and their contributions to complex "Frontal Lobe" tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100.Google Scholar
Mungas, D., Widaman, K., Zelazo, P.D., Tulsky, D., Heaton, R.K., Slotkin, J., … Gershon, R.C. (2013). NIH toolbox cognitive health battery (CB): Factor structure for 3- to 15 year olds. Monographs Society for Research on Child Development, 78(4), 103–118. Chapter VII.Google Scholar
Mungas, D., Widaman, K.F., Reed, B.R., & Tomaszewski Farias, S. (2011). Measurement invariance of neuropsychological tests in diverse older persons. Neuropsychology, 25(2), 260–269.Google Scholar
Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.Google Scholar
Salthouse, T.A. (2005). Relations between cognitive abilities and measures of executive functioning. Neuropsychology, 19(4), 532–545.Google Scholar
Smith, G.E., Ivnik, R.J., Malec, J.F., Kokmen, E., Tangalos, E.G., & Kurland, L.T. (1992). Mayo’s Older Americans Normative Studies (MOANS): Factor structure of a core battery. Psychological Assessment, 4(3), 382–390.Google Scholar
Smith, G.E., Ivnik, R.J., Malec, J.F., & Tangalos, E.G. (1993). Factor structure of the Mayo Older Americans Normative Sample (MOANS) core battery: Replication in a clinical sample. Psychological Assessment, 5(1), 121–124.Google Scholar
Steiger, J.H., Shapiro, A., & Browne, M.W. (1985). On the multivariate asymptotic distribution of sequential chi-square statistics. Psychometrika, 50, 253–264.Google Scholar
Tucker, L.R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.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(3), 416–425.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale IV. San Antonio, TX: Harcourt Assessment.Google Scholar
Wilkinson, G.S., & Robertson, G.J. (2006). WRAT 4: Wide range achievement test professional manual. Lutz, FL: Psychological Assessment Resources.Google Scholar