Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T16:36:49.805Z Has data issue: false hasContentIssue false
  • English
  • Français

The Uniform Dataset 3.0 Neuropsychological Battery: Factor Structure, Invariance Testing, and Demographically Adjusted Factor Score Calculation

Published online by Cambridge University Press:  17 February 2020

Andrew M. Kiselica Open the ORCID record for Andrew M. Kiselica [Opens in a new window] ,
Troy A. Webber  and
Jared F. Benge
Andrew M. Kiselica*
Affiliation:
Division of Neuropsychology, Department of Neurology, Baylor Scott and White Health, Temple, TX, USA
Troy A. Webber
Affiliation:
Michael E. DeBakey VA Medical Center, Mental Health Care Line, Houston, TX, USA
Jared F. Benge
Affiliation:
Division of Neuropsychology, Department of Neurology, Baylor Scott and White Health, Temple, TX, USA Plummer Movement Disorders Center, Temple, TX, USA Texas A&M College of Medicine, Temple, TX, USA
*
*Correspondence and reprint requests to: Andrew M. Kiselica, Division of Neuropsychology, Baylor Scott and White Health, 2301 S. 31st Street, Temple, TX76508. E-mail: andrew.kiselica@bswhealth.org
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

The goals of this study were to (1) specify the factor structure of the Uniform Dataset 3.0 neuropsychological battery (UDS3NB) in cognitively unimpaired older adults, (2) establish measurement invariance for this model, and (3) create a normative calculator for factor scores.

Methods:

Data from 2520 cognitively intact older adults were submitted to confirmatory factor analyses and invariance testing across sex, age, and education. Additionally, a subsample of this dataset was used to examine invariance over time using 1-year follow-up data (n = 1061). With the establishment of metric invariance of the UDS3NB measures, factor scores could be extracted uniformly for the entire normative sample. Finally, a calculator was created for deriving demographically adjusted factor scores.

Results:

A higher order model of cognition yielded the best fit to the data χ2(47) = 385.18, p < .001, comparative fit index = .962, Tucker-Lewis Index = .947, root mean square error of approximation = .054, and standardized root mean residual = .036. This model included a higher order general cognitive abilities factor, as well as lower order processing speed/executive, visual, attention, language, and memory factors. Age, sex, and education were significantly associated with factor score performance, evidencing a need for demographic correction when interpreting factor scores. A user-friendly Excel calculator was created to accomplish this goal and is available in the online supplementary materials.

Conclusions:

The UDS3NB is best characterized by a higher order factor structure. Factor scores demonstrate at least metric invariance across time and demographic groups. Methods for calculating these factors scores are provided.

Keywords

Uniform DatasetNeuropsychological testsFactor analysisCognitionDemographic correctionMeasurement invariance
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 26 , Issue 6 , July 2020 , pp. 576 - 586
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

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 Educational Research Association, American Psychological Association, National Council on Measurement in Education, & Joint Committee on Standards for Educational and Psychological Testing. (2014). The Standards for Educational and Psychological Testing. Washington, DC: AERA.Google Scholar
Besser, L., Kukull, W., Knopman, D.S., Chui, H., Galasko, D., Weintraub, S., Jicha, G., Carlsson, C., Burns, J., Quinn, J., Sweet, R.A., Rascovsky, K., Teylan, M., Beekly, D., Thomas, G., Bollenbeck, M., Monsell, S., Mock, C., Zhou, X.H., Thomas, N., Robichaud, E., Dean, M., Hubbard, J., Jacka, M., Schwabe-Fry, K., Wu, J., Phelps, C., Morris, J.C., Neuropsychology Work Group, Directors, & Clinical Core leaders of the National Institute on Aging-funded US Alzheimer’s Disease Centers. (2018). Version 3 of the National Alzheimer’s Coordinating Center’s uniform data set. Alzheimer Disease and Associated Disorders, 32(4), 351–358. doi: 10.1097/WAD.0000000000000279Google ScholarPubMed
Blankson, A.N. & McArdle, J.J. (2015). Measurement invariance of cognitive abilities across ethnicity, gender, and time among older Americans. Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 70(3), 386–397. doi: 10.1093/geronb/gbt106CrossRefGoogle ScholarPubMed
Chen, F.F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. doi: 10.1080/10705510701301834CrossRefGoogle Scholar
Cheung, G.W. & Rensvold, R.B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. doi: 10.1207/S15328007SEM0902_5CrossRefGoogle Scholar
Craft, S., Newcomer, J., Kanne, S., Dagogo-Jack, S., Cryer, P., Sheline, Y., Luby, J., Dagogo-Jack, A., & Alderson, A. (1996). Memory improvement following induced hyperinsulinemia in Alzheimer’s disease. Neurobiology of Aging, 17(1), 123–130. doi: 10.1016/0197-4580(95)02002-0CrossRefGoogle ScholarPubMed
Cronbach, L.J. & Meehl, P.E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. doi: 10.1037/h0040957CrossRefGoogle ScholarPubMed
Cummings, J., Lee, G., Ritter, A., Sabbagh, M., & Zhong, K. (2019). Alzheimer’s disease drug development pipeline: 2019. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5, 272–293. doi: 10.1016/j.trci.2019.05.008Google ScholarPubMed
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(6), 936–946. doi: 10.1017/S1355617703960139CrossRefGoogle ScholarPubMed
Devora, P.V., Beevers, S., Kiselica, A.M., & Benge, J.F. (2019). Normative data for derived measures and discrepancy scores for the Uniform Data Set 3.0 Neuropsychological Battery. Archives of Clinical Neuropsychology, epublication ahead of print. doi: 10.1093/arclin/acz025Google ScholarPubMed
DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: considerations for the applied researcher. Practical Assessment, Research & Evaluation, 14(20), 1–11.Google Scholar
Dowling, N.M., Hermann, B., La Rue, A., & Sager, M.A. (2010). Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer’s disease. Neuropsychology, 24(6), 742–756. doi: 10.1037/a0020176CrossRefGoogle Scholar
Duff, K., Schoenberg, M.R., Mold, J.W., Scott, J.G., & Adams, R.L. (2011). Gender differences on the repeatable battery for the assessment of neuropsychological status subtests in older adults: baseline and retest data. Journal of Clinical and Experimental Neuropsychology, 33(4), 448–455. doi: 10.1080/13803395.2010.533156CrossRefGoogle ScholarPubMed
Ferreira, L., Santos-Galduróz, R.F., Ferri, C.P., & Galduróz, J.C.F. (2014). Rate of cognitive decline in relation to sex after 60 years-of-age: a systematic review. Geriatrics & Gerontology International, 14(1), 23–31. doi: 10.1111/ggi.12093CrossRefGoogle ScholarPubMed
Fillenbaum, G., Peterson, B., & Morris, J. (1996). Estimating the validity of the clinical dementia rating scale: the CERAD experience. Aging Clinical and Experimental Research, 8(6), 379–385. doi: 10.1007/bf03339599CrossRefGoogle ScholarPubMed
Gavett, B.E., Vudy, V., Jeffrey, M., John, S.E., Gurnani, A.S., & Adams, J.W. (2015). The δ latent dementia phenotype in the uniform data set: cross-validation and extension. Neuropsychology, 29(3), 344–352. doi: 10.1037/neu0000128CrossRefGoogle Scholar
Gollan, T.H., Weissberger, G.H., Runnqvist, E., Montoya, R.I., & Cera, C.M. (2012). Self-ratings of spoken language dominance: a Multilingual Naming Test (MINT) and preliminary norms for young and aging Spanish–English bilinguals. Bilingualism: Language and Cognition, 15(3), 594–615. doi: 10.1017/S1366728911000332CrossRefGoogle Scholar
Goodglass, H., Kaplan, E., & Barresi, B. (2000). Boston Diagnostic Aphasia Examination Record Booklet. Philadelphia, PA: Lippincott Williams & Wilkins.Google Scholar
Grice, J.W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430–450.CrossRefGoogle ScholarPubMed
Harnack, L., Story, M., Martinson, B., Neumark-Sztainer, D., & Stang, J. (1998). Guess who’s cooking? The role of men in meal planning, shopping, and preparation in US families. Journal of the American Dietetic Association, 98(9), 995–1000. doi: 10.1016/S0002-8223(98)00228-4CrossRefGoogle ScholarPubMed
Hayden, K.M., Jones, R.N., Zimmer, C., Plassman, B.L., Browndyke, J.N., Pieper, C., Warren, L.H., & 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(2), 128–137. doi: 10.1097/WAD.0b013e3181ffa76dCrossRefGoogle ScholarPubMed
Hayden, K.M., Kuchibhatla, M., Romero, H.R., Plassman, B.L., Burke, J.R., Browndyke, J.N., & Welsh-Bohmer, K.A. (2014). Pre-clinical cognitive phenotypes for Alzheimer disease: a latent profile approach. The American Journal of Geriatric Psychiatry, 22(11), 1364–1374. doi: 10.1016/j.jagp.2013.07.008CrossRefGoogle ScholarPubMed
Henry, J.D. & Crawford, J.R. (2004). A meta-analytic review of verbal fluency performance following focal cortical lesions. Neuropsychology, 18(2), 284–295. doi: 10.1037/0894-4105.18.2.284CrossRefGoogle ScholarPubMed
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: guidelines for determining model fit. Journal of Business Research Methods, 6(1), 53–60.Google Scholar
Hu, L.-t. & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. doi: 10.1080/10705519909540118CrossRefGoogle Scholar
IBM Corp. (2017). SPSS Statistics 25.0. Armonk, NY.Google Scholar
Ivanova, I., Salmon, D.P., & Gollan, T.H. (2013). The multilingual naming test in Alzheimer’s disease: clues to the origin of naming impairments. Journal of the International Neuropsychological Society, 19(3), 272–283. doi: 10.1017/S1355617712001282CrossRefGoogle ScholarPubMed
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D.M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J.L., Montine, T., Phelps, C., Rankin, K.P., Rowe, C.C., Scheltens, P., Siemers, E., Snyder, H.M., Sperling, R., Elliott, C., Masliah, E., Ryan, L.M., & Silverberg, N. (2018). NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers & Dementia, 14(4), 535–562. doi: 10.1016/j.jalz.2018.02.018CrossRefGoogle Scholar
Jacobson, M.W., Delis, D.C., Hamilton, J.M., Bondi, M.W., & Salmon, D.P. (2004). How do neuropsychologists define cognitive constructs? Further thoughts on limitations of factor analysis used with normal or mixed clinical populations. Journal of the International Neuropsychological Society, 10(7), 1020–1021. doi: 10.1017/S1355617704107121CrossRefGoogle Scholar
Jensen, A.R. (1998). The g Factor: The Science of Mental Ability. Westport, CT: Praeger Publishers.Google Scholar
Kaplan, R.F., Cohen, R.A., Moscufo, N., Guttmann, C., Chasman, J., Buttaro, M., Hall, C.H., & Wolfson, L. (2009). Demographic and biological influences on cognitive reserve. Journal of Clinical and Experimental Neuropsychology, 31(7), 868–876. doi: 10.1080/13803390802635174CrossRefGoogle ScholarPubMed
Kiselica, A.M., Kaser, A., & Benge, J.F. (in preparation). An initial empirical operationalization of the earliest stages of the Alzheimer’s Continuum.Google Scholar
Kiselica, A.M., Webber, T.A., & Benge, J.F. (under review). Multivariate base rates of low scores in the Uniform Data Set 3.0 Neuropsychological Battery: Normative data and predictive validity for cognitive decline.Google Scholar
Kline, R.B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: Guilford Press.Google Scholar
Lenehan, M.E., Summers, M.J., Saunders, N.L., Summers, J.J., & Vickers, J.C. (2015). Relationship between education and age-related cognitive decline: A review of recent research. Psychogeriatrics, 15(2), 154–162. doi: 10.1111/psyg.12083CrossRefGoogle ScholarPubMed
Lezak, M., Howieson, D., & Loring, D. (2012). Neuropsychological Assessment (5th ed.). New York: Oxford.Google Scholar
Li, C.-L. & Hsu, H.-C. (2015). Cognitive function and associated factors among older people in Taiwan: Age and sex differences. Archives of Gerontology and Geriatrics, 60(1), 196–200. doi: 10.1016/j.archger.2014.10.007CrossRefGoogle ScholarPubMed
Little, T.D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32(1), 53–76. doi: 10.1207/s15327906mbr3201_3CrossRefGoogle ScholarPubMed
Monsell, S.E., Dodge, H.H., Zhou, X.-H., Bu, Y., Besser, L.M., Mock, C., Hawes, S.E., Kukull, W.A., & Weintraub, S. (2016). Results from the NACC Uniform Data Set neuropsychological battery crosswalk study. Alzheimer Disease and Associated Disorders, 30(2), 134–139. doi: 10.1097/WAD.0000000000000111CrossRefGoogle ScholarPubMed
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414. doi: 10.1212/wnl.43.11.2412-aCrossRefGoogle ScholarPubMed
Morris, J.C. (1997). Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. International Psychogeriatrics, 9(S1), 173–176. doi: 10.1017/s1041610297004870CrossRefGoogle ScholarPubMed
Munro, C.A., Winicki, J.M., Schretlen, D.J., Gower, E.W., Turano, K.A., Muñoz, B., Keay, L., Bandeen-Roche, K., & West, S.K. (2012). Sex differences in cognition in healthy elderly individuals. Aging, Neuropsychology, and Cognition, 19(6), 759–768. doi: 10.1080/13825585.2012.690366CrossRefGoogle ScholarPubMed
Niileksela, C.R., Reynolds, M.R., & Kaufman, A.S. (2013). An alternative Cattell-Horn-Carroll (CHC) factor structure of the WAIS-IV: Age invariance of an alternative model for ages 70–90. Psychological Assessment, 25(2), 391–404. doi: 10.1037/a0031175CrossRefGoogle ScholarPubMed
Partington, J.E. & Leiter, R.G. (1949). Partington pathways test. Psychological Service Center Journal, 1, 11–20.Google Scholar
Possin, K.L., Laluz, V.R., Alcantar, O.Z., Miller, B.L., & Kramer, J.H. (2011). Distinct neuroanatomical substrates and cognitive mechanisms of figure copy performance in Alzheimer’s disease and behavioral variant frontotemporal dementia. Neuropsychologia, 49(1), 43–48. doi: 10.1016/j.neuropsychologia.2010.10.026CrossRefGoogle ScholarPubMed
Proust-Lima, C., Amieva, H., Letenneur, L., Orgogozo, J.-M., Jacqmin-Gadda, H., & Dartigues, J.-F. (2008). Gender and education impact on brain aging: A general cognitive factor approach. Psychology and Aging, 23(3), 608–620. doi: 10.1037/a0012838.suppCrossRefGoogle ScholarPubMed
Rawlings, A.M., Bandeen-Roche, K., Gross, A.L., Gottesman, R.F., Coker, L.H., Penman, A. D., Sharrett, A.R., & Mosley, T. H. (2016). Factor structure of the ARIC-NCS neuropsychological battery: An evaluation of invariance across vascular factors and demographic characteristics. Psychological Assessment, 28(12), 1674–1683. doi: 10.1037/pas0000293CrossRefGoogle ScholarPubMed
Reeve, C.L. & Blacksmith, N. (2009). Identifying g: A review of current factor analytic practices in the science of mental abilities. Intelligence, 37(5), 487–494. doi: 10.1016/j.intell.2009.06.002CrossRefGoogle Scholar
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2), 1–36. doi: 10.18637/jss.v048.i02CrossRefGoogle Scholar
RStudio Team. (2015). RStudio: Integrated Development for R. Boston, MA: RStudio Inc. Retrieved from http://www.rstudio.com/.Google Scholar
Salthouse, T.A. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16(5), 754–760.CrossRefGoogle ScholarPubMed
Schneider, W.J. & McGrew, K.S. (2018). The Cattell-Horn-Carroll theory of cognitive abilities. In Flanagan, D.P. & McDonough, E.M. (2018). The Cattell-Horn-Carroll theory of cognitive abilities. In (Eds.), Contemporary Intellectual Assessment: Theories, Tests, and Issues (4th ed., pp. 73–163). New York: The Guilford Press.Google Scholar
Shin, M.S., Park, S.Y., Park, S.R., Seol, S.H., & Kwon, J.S. (2006). Clinical and empirical applications of the Rey-Osterrieth complex figure test. Nature Protocols, 1(2), 892–899. doi: 10.1038/nprot.2006.115CrossRefGoogle ScholarPubMed
Shirk, S.D., Mitchell, M.B., Shaughnessy, L.W., Sherman, J.C., Locascio, J.J., Weintraub, S., & Atri, A. (2011). A web-based normative calculator for the Uniform Dataset (UDS) neuropsychological test battery. Alzheimer’s Research & Therapy, 3(6), 32. doi: 10.1186/alzrt94CrossRefGoogle Scholar
Thompson, B. (2004). Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications. Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Thompson, B. & Daniel, L.G. (1996). Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines. Sage Publications: Thousand Oaks, CA.Google Scholar
Tripathi, R., Kumar, K., Bharath, S., Marimuthu, P., & Varghese, M. (2014). Age, education and gender effects on neuropsychological functions in healthy Indian older adults. Dementia & Neuropsychologia, 8(2), 148–154. doi: 10.1590/S1980-57642014DN82000010CrossRefGoogle ScholarPubMed
van de Schoot, R., Lugtig, P., & Hox, J. (2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9(4), 486–492. doi: 10.1080/17405629.2012.686740CrossRefGoogle Scholar
Varjacic, A., Mantini, D., Demeyere, N., & Gillebert, C.R. (2018). Neural signatures of Trail Making Test performance: evidence from lesion-mapping and neuroimaging studies. Neuropsychologia, 115, 78–87. doi: 10.1016/j.neuropsychologia.2018.03.031CrossRefGoogle ScholarPubMed
Wechsler, D. (1987). Wechsler Memory Scale-revised Manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D. (2008). Wechsler Adult Intelligence Scale–Fourth Edition (WAIS–IV). San Antonio, TX: The Psychological Corporation.Google Scholar
Weintraub, S., Besser, L., Dodge, H.H., Teylan, M., Ferris, S., Goldstein, F.C., Giordani, B., Kramer, J., Loewenstein, D., Marson, D., Mungas, D., Salmon, D., Welsh-Bohmer, K., Zhou, X.H., Shirk, S.D., Atri, A., Kukull, W.A., Phelps, C., & Morris, J.C. (2018). Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Disease and Associated Disorders, 32(1), 10–17. doi: 10.1097/WAD.0000000000000223CrossRefGoogle Scholar
Weintraub, S., Salmon, D., Mercaldo, N., Ferris, S., Graff-Radford, N. R., Chui, H., Cummings, J., DeCarli, C., Foster, N.L., Galasko, D., Peskind, E., Dietrich, W., Beekly, D.L., Kukull, W.A., & Morris, J.C. (2009). The Alzheimer’s disease centers’ uniform data set (UDS): The neuropsychological test battery. Alzheimer Disease and Associated Disorders, 23(2), 91–101. doi: 10.1097/WAD.0b013e318191c7ddCrossRefGoogle Scholar
Supplementary material: File

Kiselica et al. supplementary material

Kiselica et al. supplementary material

Download Kiselica et al. supplementary material(File)
File 63.9 KB