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Factor analysis of the Cognitive Abilities Screening Instrument: Kuakini Honolulu-Asia Aging Study

Published online by Cambridge University Press:  25 June 2020

Hardeep K. Obhi Open the ORCID record for Hardeep K. Obhi [Opens in a new window] ,
Jennifer A. Margrett ,
Daniel W. Russell ,
Peter Martin ,
Leonard W. Poon ,
Kamal Masaki  and
Bradley J. Willcox
Hardeep K. Obhi*
Affiliation:
Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
Jennifer A. Margrett
Affiliation:
Department of Human Development and Family Studies, Iowa State University, Ames, IA, USA
Daniel W. Russell
Affiliation:
Department of Human Development and Family Studies, Iowa State University, Ames, IA, USA
Peter Martin
Affiliation:
Department of Human Development and Family Studies, Iowa State University, Ames, IA, USA
Leonard W. Poon
Affiliation:
Institute of Gerontology, University of Georgia, Athens, GA, USA
Kamal Masaki
Affiliation:
John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center, Honolulu, HI, USA
Bradley J. Willcox
Affiliation:
John A. Burns School of Medicine, University of Hawaii, Kuakini Medical Center, Honolulu, HI, USA
*
Correspondence should be addressed to: Hardeep K. Obhi, Postdoctoral Research Associate, Carolina Population Center #3105H, 123 West Franklin Street, The University of North Carolina at Chapel Hill, Chapel Hill, NC27516, USA. Phone: +1 919 962 6111. Email: [email protected]
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Abstract

Objective:

The Cognitive Abilities Screening Instrument (CASI) is a screening test of global cognitive function used in research and clinical settings. However, the CASI was developed using face validity and has not been investigated via empirical tests such as factor analyses. Thus, we aimed to develop and test a parsimonious conceptualization of the CASI rooted in cognitive aging literature reflective of crystallized and fluid abilities.

Design:

Secondary data analysis implementing confirmatory factor analyses where we tested the proposed two-factor solution, an alternate one-factor solution, and conducted a χ2 difference test to determine which model had a significantly better fit.

Setting:

N/A.

Participants:

Data came from 3,491 men from the Kuakini Honolulu-Asia Aging Study.

Measurements:

The Cognitive Abilities Screening Instrument.

Results:

Findings demonstrated that both models fit the data; however, the two-factor model had a significantly better fit than the one-factor model. Criterion validity tests indicated that participant age was negatively associated with both factors and that education was positively associated with both factors. Further tests demonstrated that fluid abilities were significantly and negatively associated with a later-life dementia diagnosis.

Conclusions:

We encourage investigators to use the two-factor model of the CASI as it could shed light on underlying cognitive processes, which may be more informative than using a global measure of cognition.

Keywords

Cognitive Abilities Screening Instrumentcognitionolder adultsfactor analysiscrystallized and fluid abilities
Type
Original Research Article
Information
International Psychogeriatrics , Volume 34 , Special Issue 6: Issue Theme: New Studies of Mild Cognitive Impairment , June 2022 , pp. 543 - 551
Copyright
© International Psychogeriatric Association 2020

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Footnotes

Research for this manuscript was conducted while Obhi was at Iowa State University, Ames, IA, USA.

References

Abbott, R.D., White, L.R., Ross, G.W., Masaki, K.H., Curb, J.D. and Petrovitch, H. (2004). Walking and dementia in physically capable elderly men. The Journal of the American Medical Association, 292(12), 1447–1453. doi: 10.1001/jama.292.12.1447CrossRefGoogle ScholarPubMed
Baghaei, P. and Tabatabaee, M. (2015). The C-Test: an integrative measure of crystallized intelligence. Journal of Intelligence, 3(2), 46–58. doi: 10.3390/jintelligence3020046CrossRefGoogle Scholar
Ball, K. et al. (2002). Effects of cognitive training interventions with older adults: a randomized controlled trial. The Journal of the American Medical Association, 288(18), 2271–2281. doi: 10.1001/jama.288.18.2271CrossRefGoogle ScholarPubMed
Baltes, P.B. (1987). Theoretical propositions of life-span developmental psychology: on the dynamics between growth and decline. Developmental Psychology, 23(5), 611. doi: 10.1037/0012-1649.23.5.611CrossRefGoogle Scholar
Baltes, P.B. (1993). The aging mind: potential and limits. The Gerontologist, 33(5), 580–594. doi: 10.1093/geront/33.5.580CrossRefGoogle ScholarPubMed
Baltes, P.B., Cornelius, S.W., Spiro, A., Nesselroade, J.R. and Willis, S.L. (1980). Integration versus differentiation of fluid/crystallized intelligence in old age. Developmental Psychology, 16(6), 625. doi: 10.1037/0012-1649.16.6.625CrossRefGoogle Scholar
Bendayan, R., Piccinin, A.M., Hofer, S.M., Cadar, D., Johansson, B. and Muniz-Terrera, G. (2017). Decline in memory, visuospatial ability, and crystallized cognitive abilities in older adults: normative aging or terminal decline? Journal of Aging Research, 2017, 1–9. doi: 10.1155/2017/6210105CrossRefGoogle ScholarPubMed
Benton, S.L., Glover, J.A. and Bruning, R.H. (1983). Levels of processing: effect of number of decisions on prose recall. Journal of Educational Psychology, 75(3), 382. doi: 10.1037/0022-0663.75.3.382CrossRefGoogle Scholar
Brugnolo, A. et al. (2009). The factorial structure of the mini mental state examination (MMSE) in Alzheimer’s disease. Archives of Gerontology and Geriatrics, 49(1), 180–185. doi: 10.1016/j.archger.2008.07.005CrossRefGoogle Scholar
Butler, M. et al. (2018). Does cognitive training prevent cognitive decline? A systematic review. Annals of Internal Medicine, 168(1), 63–68. doi: 10.7326/M17-1531CrossRefGoogle ScholarPubMed
Cohen, R.J. and Swerdlik, M.E. (2005). Psychological testing and assessment: an introduction to tests and measurement (6th ed.). New York: McGraw-Hill.Google Scholar
Craik, F.I.M. and Byrd, M. (1982). Aging and cognitive deficits: the role of attentional resources. In: Craik, F.I.M. and Trehub, S. (Eds.), Aging and Cognitive Processes: Advances In the Study of Communication and Affect. Boston, MA: Springer.10.1007/978-1-4684-4178-9CrossRefGoogle Scholar
Davey, A. et al. (2010). Cognitive function, physical performance, health, and disease: norms from the georgia centenarian study. Experimental Aging Research, 36(4), 394–425. doi: 10.1080/0361073X.2010.509010CrossRefGoogle ScholarPubMed
de Frias, C.M., Lövdén, M., Lindenberger, U. and Nilsson, L.G. (2007). Revisiting the dedifferentiation hypothesis with longitudinal multi-cohort data. Intelligence, 35(4), 381–392. doi: 10.1016/j.intell.2006.07.011CrossRefGoogle Scholar
Flanagan, D.P. and Dixon, S.G. (2013). The Cattell-Horn-Carroll theory of cognitive abilities. In: Reynolds, C.R., Vannest, K.J. and Fletcher-Janzen, E. (Eds.), Encyclopedia of Special Education: A Reference for the Education of Children, Adolescents, and Adults With Disabilities and Other Exceptional Individuals (pp. 368–382). Hoboken, NJ: John Wiley & Sons.Google Scholar
Folstein, M.F., Folstein, S.E. and McHugh, P.R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. doi: 10.1016/0022-3956(75)90026-6CrossRefGoogle ScholarPubMed
Gelber, P.R., Launer, J.L. and White, R.L. (2012). The Honolulu-Asia aging study: epidemiologic and neuropathologic research on cognitive impairment. Current Alzheimer Research, 9(6), 664–672. doi: https://doi.org/10.2174/156720512801322618CrossRefGoogle ScholarPubMed
Glisky, E.L. (2007). Changes in cognitive function and human aging. In: Riddle, D.R (Ed.), Brain Aging: Models, Methods, and Mechanisms (pp. 3–20). Boca Raton, FL: CRC Press.Google ScholarPubMed
Hartshorne, J.K. and Germine, L.T. (2015). When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychological Science, 26(4), 433–443. doi: 10.1177/0956797614567339CrossRefGoogle ScholarPubMed
Hasegawa, K. (1983). The clinical assessment of dementia in the aged: a dementia screening scale for psychogeriatric patients. In: Bergener, M. et al. (Eds.), Aging in the Eighties and Beyond (pp. 207–218). New York: Springer.Google Scholar
Hu, L.T. and Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. doi: 10.1080/10705519909540118CrossRefGoogle Scholar
Hülür, G., Ram, N., Willis, S.L., Schaie, K.W. and Gerstof, D. (2015). Cognitive dedifferentiation with increasing age and proximity of death: within-person evidence from the seattle longitudinal study. Psychology and Aging, 30(2), 311. doi: 10.1037/a0039260CrossRefGoogle Scholar
Kaufman, A.S., Kaufman, J.C. and McLean, J.E. (1995). Factor structure of the kaufman adolescent and adult intelligence test (KAIT) for whites, African Americans, and Hispanics. Educational and Psychological Measurement, 55(3), 365–376. doi: 10.1177/0013164495055003001CrossRefGoogle Scholar
Kuakini Health System. (2018). Kuakini Honolulu Heart Program [Online]. Available at: http://www.kuakini.org/wps/portal/kuakini-research/research-home/kuakini-research-programs/kuakini-honolulu-heart-program.Google Scholar
Launer, L.J., Masaki, K., Petrovitch, H., Foley, D. and Havlik, R.J. (1995). The association between midlife blood pressure levels and late-life cognitive function: the Honolulu-Asia aging study. The Journal of the American Medical Association, 274(23), 1846–1851. doi: 10.1001/jama.1995.03530230032026CrossRefGoogle ScholarPubMed
Laurin, D., Curb, J.D., Masaki, K.H., White, L.R. and Launer, L.J. (2009). Midlife C-reactive protein and risk of cognitive decline: a 31-year follow-up. Neurobiology of Aging, 30(11), 1724–1727. doi: 10.1016/j.neurobiolaging.2008.01.008CrossRefGoogle ScholarPubMed
Lima, S.D., Hale, S. and Myerson, J. (1991). How general is general slowing? Evidence from the lexical domain. Psychology and Aging, 6(3), 416. doi: 10.1037/0882-7974.6.3.416CrossRefGoogle ScholarPubMed
Masaki, K.H. et al. (2000). Association of vitamin E and C supplement use with cognitive function and dementia in elderly men. Neurology, 54(6), 1265–1272. doi: 10.1212/WNL.54.6.1265CrossRefGoogle Scholar
McDonough, I.M. et al. (2016). Discrepancies between fluid and crystallized ability in healthy adults: a behavioral marker of preclinical Alzheimer’s disease. Neurobiology of Aging, 46, 68–75. doi: 10.1016/j.neurobiolaging.2016.06.011CrossRefGoogle ScholarPubMed
Murman, D.L. (2015). The impact of age on cognition. Seminars in Hearing, 36(3), 111–121. doi: 10.1055/s-0035-1555115Google ScholarPubMed
Muthén, L.K. and Muthén, B.O. (1998–2011). Mplus User’s Guide (7th ed). Los Angeles: Muthén & Muthén.Google Scholar
Niileksela, C.R., Reynolds, M.R. and 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. doi: 10.1037/a0031175CrossRefGoogle ScholarPubMed
Parkin, A.J. and Java, R.I. (1999). Deterioration of frontal lobe function in normal aging: influences of fluid intelligence versus perceptual speed. Neuropsychology, 13(4), 539. doi: 10.1037/0894-4105.13.4.539CrossRefGoogle ScholarPubMed
Parkin, A.J., Walter, B.M. and Hunkin, N.M. (1995). Relationships between normal aging, frontal lobe function, and memory for temporal and spatial information. Neuropsychology, 9(3), 304. doi: 10.1037/0894-4105.9.3.304CrossRefGoogle Scholar
Park, D.C. and Festini, S.B. (2017). Theories of memory and aging: a look at the past and a glimpse of the future. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 72(1), 82–90. doi: 10.1093/geronb/gbw066CrossRefGoogle Scholar
Raz, A. (2009). Varieties of attention. In: Cacioppo, J.T. and Berntson, G. (Eds.), Handbook of Neuroscience for the Behavioral Sciences (pp. 361–370). Hoboken, NJ: John Wiley & Sons.Google Scholar
Salthouse, T.A. (2004). What and when of cognitive aging. Current Directions in Psychological Science, 13(4), 140–144. doi: 10.1111/j.0963-7214.2004.00293.xCrossRefGoogle Scholar
Schaie, K.W. (1994). The course of adult intellectual development. American Psychologist, 49(4), 304. doi: 10.1037/0003-066X.49.4.304CrossRefGoogle ScholarPubMed
Staudinger, U.M., Cornelius, S.W. and Baltes, P.B. (1989). The aging of intelligence: Potential and limits. The Annals of the American Academy of Political and Social Science, 503(1), 43–59. doi: 10.1177/0002716289503001004CrossRefGoogle Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448–460. doi: 10.1017.S1355617701020240CrossRefGoogle ScholarPubMed
Tadayon, E., Pascual-Leone, A. and Santarnecchi, E. (2020). Differential contribution of cortical thickness, surface area, and gyrification to fluid and crystallized intelligence. Cerebral Cortex, 30(1), 215–225. doi: 10.1093/cercor/bhz082CrossRefGoogle ScholarPubMed
Teng, E.L. and Chui, H.C. (1987). The modified mini-mental state (3MS) examination. The Journal of Clinical Psychiatry, 48(8), 314–318.Google ScholarPubMed
Teng, E.L. et al. (1994). The cognitive abilities screening instrument (CASI): a practical test for cross-cultural epidemiological studies of dementia. International Psychogeriatrics, 6(1), 45–58. doi: 10.1017/S1041610294001602CrossRefGoogle ScholarPubMed
Thorvaldsson, V. et al. (2011). Onset and rate of cognitive change before dementia diagnosis: findings from two Swedish population-based longitudinal studies. Journal of the International Neuropsychological Society, 17(1), 154–162. doi: 10.1017/S1355617710001372CrossRefGoogle ScholarPubMed
Tsai, R.C., Lin, K.N., Wang, H.J. and Liu, H.C. (2007). Evaluating the uses of the total score and the domain scores in the cognitive abilities screening instrument, chinese version (CASI C-2.0): results of confirmatory factor analysis. International Psychogeriatrics, 19(6), 1051–1063. doi: 10.1017/S1041610207005327CrossRefGoogle ScholarPubMed
Tucker-Drob, E.M. (2009). Differentiation of cognitive abilities across the life span. Developmental Psychology, 45(4), 1097. doi: 10.1037/a0015864CrossRefGoogle ScholarPubMed
Undheim, J.O. (1976). Ability structure in 10-11-year-old children and the theory of fluid and crystallized intelligence. Journal of Educational Psychology, 68(4), 411–423. doi: 10.1037/0022-0663.68.4.411CrossRefGoogle Scholar
Unsworth, N. (2010). On the division of working memory and long-term memory and their relation to intelligence: a latent variable approach. Acta Psychologica, 134(1), 16–28. doi: 10.1016/j.actpsy.2009.11.010CrossRefGoogle ScholarPubMed
Vallesi, A. (2016). Dual-task costs in aging are predicted by formal education. Aging Clinical and Experimental Research, 28(5), 959–964. doi: 10.1007/s40520-015-0385-5CrossRefGoogle ScholarPubMed
White, L. et al. (1996). Prevalence of dementia in older Japanese American men in Hawaii: the Honolulu-Asia aging study. The Journal of the American Medical Association, 276(12), 955–960. doi: 10.1001/jama.1996.03540120033030CrossRefGoogle ScholarPubMed
Wilson, R.S., Segawa, E., Hizel, L.P., Boyle, P.A. and Bennett, D.A. (2012). Terminal dedifferentiation of cognitive abilities. Neurology, 78(15), 1116–1122. doi: 10.1212/WNL.0b013e31824f7ff2CrossRefGoogle ScholarPubMed
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