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Language Measures of the NIH Toolbox Cognition Battery

Published online by Cambridge University Press:  24 June 2014

Richard C. Gershon ,
Karon F. Cook ,
Dan Mungas ,
Jennifer J. Manly ,
Jerry Slotkin ,
Jennifer L. Beaumont  and
Sandra Weintraub
Richard C. Gershon*
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Karon F. Cook
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Dan Mungas
Affiliation:
Department of Neurology, University of California, Davis, California
Jennifer J. Manly
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York
Jerry Slotkin
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Jennifer L. Beaumont
Affiliation:
Department of Medical Social Sciences, Northwestern University, Chicago, Illinois
Sandra Weintraub
Affiliation:
Cognitive Neurology and Alzheimer’s Disease Center, Northwestern Feinberg School of Medicine, Chicago, Illinois
*
Correspondence and reprint requests to: Richard Gershon, Northwestern University, Suite 2700, 625 North Michigan Avenue, Chicago, IL 60611. E-mail: [email protected]
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Abstract

Language facilitates communication and efficient encoding of thought and experience. Because of its essential role in early childhood development, in educational achievement and in subsequent life adaptation, language was included as one of the subdomains in the NIH Toolbox for the Assessment of Neurological and Behavioral Function Cognition Battery (NIHTB-CB). There are many different components of language functioning, including syntactic processing (i.e., morphology and grammar) and lexical semantics. For purposes of the NIHTB-CB, two tests of language—a picture vocabulary test and a reading recognition test—were selected by consensus based on literature reviews, iterative expert input, and a desire to assess in English and Spanish. NIHTB-CB’s picture vocabulary and reading recognition tests are administered using computer adaptive testing and scored using item response theory. Data are presented from the validation of the English versions in a sample of adults ages 20–85 years (Spanish results will be presented in a future publication). Both tests demonstrated high test–retest reliability and good construct validity compared to corresponding gold-standard measures. Scores on the NIH Toolbox measures were consistent with age-related expectations, namely, growth in language during early development, with relative stabilization into late adulthood. (JINS, 2014, 20, 1–10)

Keywords

Reading testVocabulary testNeuropsychological assessmentComputer adaptive testingItem response theorySpanish version
Type
Special Series
Information
Journal of the International Neuropsychological Society , Volume 20 , Issue 6 , July 2014 , pp. 642 - 651
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Baumann, J.F. (2009). Intensity in vocabulary instruction and effects on reading comprehension. Topics in Language Disorders, 29(4), 312–328. 310.1097/TLD.1090b1013e3181c1029e1022.Google Scholar
Benedict, R. (1997). Brief Visuospatial Memory Test – Revised: Professional manual. Odessa, FL: Psychological Assessment Resources, Inc.Google Scholar
Benedict, R.H.B., Schretlen, D., Groninger, L., Dobraski, M., & Shpritz, B. (1996). Revision of the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and validity. Psychological Assessment, 8(2), 145–153.CrossRefGoogle Scholar
Boone, K.B., Lu, P., & Wen, J. (2005). Comparison of various RAVLT scores in the detection of noncredible memory performance. Archives of Clinical Neuropsychology, 20(3), 301–319.Google Scholar
Broadley, M.E. (1994). Your natural gifts: How to recognize and develop them for success and self-fulfillment. McLean, VA: EPM Publications.Google Scholar
Burton, C.L., Strauss, E., Hultsch, D.F., & Hunter, M.A. (2006). Cognitive functioning and everyday problem solving in older adults. Clinical Neuropsychologist, 20(3), 432–452.Google Scholar
Cattell, R.B. (1987). Intelligence: Its structure, growth and action. Amsterdam: Elsevier.Google Scholar
Catts, H.W., Fey, M.E., Tomblin, J.B., & Zhang, X. (2002). A longitudinal investigation of reading outcomes in children with language impairments. Journal of Speech, Language, and Hearing Research, 45(6), 1142–1157.Google Scholar
Chiappe, P., Siegel, L.S., & Gottardo, A. (2002). Reading-related skills of kindergartners from diverse linguistic backgrounds. Applied Psycholinguistics, 23(01), 95–116.Google Scholar
Dale, E., & O’Rourke, J. (1976). The living word vocabulary: The words we know: A national vocabulary inventory. Elgin, IL: Field Enterprises Educational Corp.Google Scholar
Deyo, R.A., Diehl, A.K., Hazuda, H., & Stern, M.P. (1985). A simple language-based acculturation scale for Mexican Americans: Validation and application to health care research. American Journal of Public Health, 75(1), 51–55.CrossRefGoogle ScholarPubMed
Dunn, D.M., & Dunn, L.M. (2007). PPVT-4: Peabody picture vocabulary test (4th ed.). Minneapolis: Pearson.Google Scholar
Fenson, L., Dale, P.S., Reznick, J.S., Bates, E., Thal, D.J., & Pethick, S.J. (1994). Variability in early communicative development. Monographs of the Society for Research in Child Development, 59(5), 1–173.CrossRefGoogle ScholarPubMed
Gershon, R.C. (1988). Index of words in the Johnson O’Connor Research Foundation, Inc. Vocabulary Item Bank (Technical report 1988-3). New York, NY: Johnson O’Connor Research Foundation Human Engineering Laboratory.Google Scholar
Gershon, R.C., Cella, D., Fox, N.A., Havlik, R.J., Hendrie, H.C., & Wagster, M.V. (2010). Assessment of neurological and behavioural function: The NIH Toolbox. Lancet Neurology, 9(2), 138–139.Google Scholar
Gershon, R.C., Slotkin, J., Manly, J.J., Blitz, D.L., Beaumont, J.L., Schnipke, D., Weintraub, S. (2013). NIH Toolbox Cognition Battery (CB): Measuring language (vocabulary comprehension and reading decoding). Chapter IV, Monographs of the Society for Research in Child Development, 78(4), 49–69.CrossRefGoogle ScholarPubMed
Gleason, J.B., & Ratner, N.B. (2009). The development of language (7th ed., Boston: Pearson/Allyn and Bacon.Google Scholar
Golinkoff, R.M., & Hirsh-Pasek, K. (1999). How babies talk: The magic and mystery of language in the first three years of life. New York: Dutton.Google Scholar
Grober, E., & Sliwinski, M. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology, 13(6), 933–949.Google Scholar
Hirsh-Pasek, K., & Golinkoff, R.M. (1996). The origins of grammar: Evidence from early language comprehension. Cambridge: MIT Press.Google Scholar
Jefferson, A.L., Gibbons, L.E., Rentz, D.M., Carvalho, J.O., Manly, J., Bennett, D.A., Jones, R.N. (2011). A life course model of cognitive activities, socioeconomic status, education, reading ability, and cognition. Journal of the American Geriatrics Society, 59(8), 1403–1411.Google Scholar
Kastner, J.W., May, W., & Hildman, L. (2001). Relationship between language skills and academic achievement in first grade. Perceptual and Motor Skills, 92(2), 381–390.Google Scholar
Kontos, D.L. (2007). Investigation of validity, reliability, and practice effects of the Immediate Postconcussion Assessment and Cognitive Test (ImPACT) and Traditional Paper-Pencil Neuropsychological Tests 2014. Retrieved from https://cdr.lib.unc.edu/indexablecontent/uuid:3bbcd1dd-b3d0-4aa2-99cb-1ce42792445fGoogle Scholar
Linacre, J.M. (2005). A user's guide to WINSTEPS/MINISTEP: Rasch-model computer programs. Chicago, IL: Winsteps.Google Scholar
Mogilner, A. (1992). Children's writer’s word book. Cincinnati, OH: Writer’s Digest Books.Google Scholar
National Institutes of Health, & Northwestern University. (2006-2012a). NIH Toolbox Picture Vocabulary Test. Used with permission. Retrieved from http://www.nihtoolbox.org/WhatAndWhy/Cognition/Language/Pages/NIH-Toolbox-Picture-Vocabulary-Test.aspxGoogle Scholar
National Institutes of Health, & Northwestern University (2006-2012b). NIH Toolbox Reading Recognition Test. Used with permission. Retrieved from http://www.nihtoolbox.org/WhatAndWhy/Cognition/Language/Pages/NIH-Toolbox-Oral-Reading-Recognition-Test.aspxGoogle Scholar
Nowinski, C.J., Victorson, D., Debb, S.M., & Gershon, R. (2013). Input on NIH Toolbox criteria: Surveying the end user research community. Neurology, 80(11 Suppl. 3), S7–S12.Google Scholar
Pae, H.K., Greenberg, D., & Williams, R.S. (2012). An analysis of differential response patterns on the Peabody Picture Vocabulary Test-IIIB in struggling adult readers and third-grade children. Reading and Writing, 25(6), 1239–1258.CrossRefGoogle Scholar
Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago: University of Chicago Press.Google Scholar
Revicki, D.A., & Cella, D.F. (1997). Health status assessment for the twenty-first century: Item response theory, item banking and computer adaptive testing. Quality of Life Research, 6(6), 595–600.Google Scholar
Rey, A. (1958). L'Examen Clinique en Psychologie. Paris: Press Universitaire de France.Google Scholar
Ritchie, S.J., & Bates, T.C. (2013). Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological Science, 24(7), 1301–1308.Google Scholar
Salthouse, T.A. (1988). Effects of aging on verbal abilities: Examination of the psychometric literature. In L.L. Light & D.M. Burke (Eds.), Language, memory, and aging (pp 17–35). New York: Cambridge University Press.Google Scholar
Scarborough, H.S. (2001). Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice. In S.B. Neuman & D.K. Dickinson (Eds.), Handbook of early literacy research (pp 97–110). New York: Guilford Press.Google Scholar
Schmidt, F.L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162–173.Google Scholar
Smith, B.I., Smith, T.D., Taylor, L., & Hobby, M. (2005). Relationship between intelligence and vocabulary. Perceptual and Motor Skills, 100(1), 101–108.Google Scholar
University of Western Australia School of Psychology. (2011). MRC Psycholinguistic Database. Retrieved from http://www.psych.rl.ac.ukGoogle Scholar
Victorson, D., Manly, J., Wallner-Allen, K., Fox, N., Purnell, C., Hendrie, H.C., Gershon, R.C. (2013). Using the NIH Toolbox in special populations: Considerations for the assessment of pediatric, geriatric, culturally diverse, non-English speaking and disabled individuals. Neurology, 80(11 Suppl. 3), S13–S19.Google Scholar
Weintraub, S., Dikmen, S.S., Heaton, R.K., Tulsky, D.S., Zelazo, P.D., Bauer, P.J., Gershon, R.C. (2013). Cognition assessment using the NIH Toolbox. Neurology, 80(11 Suppl. 3), S54–S64.Google Scholar
Weintraub, S., Dikmen, S.S., Heaton, R.K., Tulsky, D.S., Zelazo, P.D., Slotkin, J., Gershon, R. (2014). The cognition battery of the NIH Toolbox for assessment of neurological and behavioral function: Validation in an adult sample. Journal of the International Neuropsychological Society.Google Scholar
Wilkinson, G.S., & Robertson, G.J. (2006). WRAT 4: Wide range achievement test professional manual. Lutz, FL: Psychological Assessment Resources, Inc.Google Scholar
Wolf, M.S., Curtis, L.M., Wilson, E.A., Revelle, W., Waite, K.R., Smith, S.G., Baker, D.W. (2012). Literacy, cognitive function, and health: Results of the LitCog study. Journal of General Internal Medicine, 27(10), 1300–1307.Google Scholar