Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-27T09:22:06.847Z Has data issue: false hasContentIssue false
  • English
  • Français

Predictors of Retest Effects in a Longitudinal Study of Cognitive Aging in a Diverse Community-Based Sample

Published online by Cambridge University Press:  01 September 2015

Alden L. Gross ,
Andreana Benitez ,
Regina Shih ,
Katherine J. Bangen ,
M. Maria M. Glymour ,
Bonnie Sachs ,
Shannon Sisco ,
Jeannine Skinner ,
Brooke C. Schneider  and
Jennifer J. Manly
Alden L. Gross*
Affiliation:
Departments of Epidemiology and Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; and Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland
Andreana Benitez
Affiliation:
Department of Radiology and Radiological Sciences, Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina
Regina Shih
Affiliation:
RAND Corporation, Arlington, Virginia
Katherine J. Bangen
Affiliation:
Department of Psychiatry, University of California, San Diego, La Jolla, California
M. Maria M. Glymour
Affiliation:
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
Bonnie Sachs
Affiliation:
Department of Neurology, Wake Forest Baptist Medical Center, Winston Salem, North Carolina
Shannon Sisco
Affiliation:
North Florida/South Georgia Veterans Health System, Department of Veterans Affairs, Gainesville, Florida
Jeannine Skinner
Affiliation:
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington
Brooke C. Schneider
Affiliation:
Department of Psychiatry and Psychotherapie, University Hospital Hamburg-Eppendorf, Hamburg, Germany
Jennifer J. Manly
Affiliation:
Taub Institute for Research on Alzheimer’s disease and the Aging Brain, Columbia University, New York, New York; Gertrude H. Sergievsky Center, Columbia University, New York, New York; and Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York
*
Correspondence and reprint requests to: Alden L. Gross, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument St., Baltimore, MD 21205, USA. E-mail: agross14@jhu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Better performance due to repeated testing can bias long-term trajectories of cognitive aging and correlates of change. We examined whether retest effects differ as a function of individual differences pertinent to cognitive aging: race/ethnicity, age, sex, language, years of education, literacy, and dementia risk factors including apolipoprotein E ε4 status, baseline cognitive performance, and cardiovascular risk. We used data from the Washington Heights-Inwood Columbia Aging Project, a community-based cohort of older adults (n=4073). We modeled cognitive change and retest effects in summary factors for general cognitive performance, memory, executive functioning, and language using multilevel models. Retest effects were parameterized in two ways, as improvement between the first and subsequent testings, and as the square root of the number of prior testings. We evaluated whether the retest effect differed by individual characteristics. The mean retest effect for general cognitive performance was 0.60 standard deviations (95% confidence interval [0.46, 0.74]), and was similar for memory, executive functioning, and language. Retest effects were greater for participants in the lowest quartile of cognitive performance (many of whom met criteria for dementia based on a study algorithm), consistent with regression to the mean. Retest did not differ by other characteristics. Retest effects are large in this community-based sample, but do not vary by demographic or dementia-related characteristics. Differential retest effects may not limit the generalizability of inferences across different groups in longitudinal research. (JINS, 2015, 21, 506–518)

Keywords

Practice effectRetest effectNeuropsychological testingOlder adultsGerontologyIndividual differences
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Supplement 7 , August 2015 , pp. 506 - 518
Copyright
Copyright © The International Neuropsychological Society 2015 

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

Abner, E.L., Dennis, B.C., Mathews, M.J., Mendiondo, M.S., Caban-Holt, A., & Kryscio, R.J., … Investigators. (2012). Practice effects in a longitudinal, multi-center Alzheimer's disease prevention clinical trial. Trials, 13, 217. doi: 10.1186/1745-6215-13-217.Google Scholar
Barnett, A.G., van der Pols, J.C., & Dobson, A.J. (2005). Regression to the mean: What it is and how to deal with it. International Journal of Epidemiology, 34(1), 215–220.CrossRefGoogle Scholar
Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., & Ehrenreich, H. (2010). Practice effects in healthy adults: A longitudinal study on frequent repetitive cognitive testing. BMC Neuroscience, 11, 118. doi: 10.1186/1471-2202-11-118 Google Scholar
Basso, M.R., Bornstein, R.A., & Lang, J.M. (1999). Practice effects on commonly used measures of executive function across twelve months. The Clinical Neuropsychologist, 13(3), 283–292. doi: 10.1076/clin.13.3.283.1743 CrossRefGoogle ScholarPubMed
Baxter, L.C., Caselli, R.J., Johnson, S.C., Reiman, E., & Osborne, D. (2003). Apolipoprotein E epsilon 4 affects new learning in cognitively normal individuals at risk for Alzheimer's disease. Neurobiology of Aging, 24(7), 947–952. doi: 10.1016/S0197-4580(03)00006-X Google Scholar
Ben-Yishay, Y., Diller, L., Mandleberg, I., Gordon, W., & Gerstman, L.J. (1974). Differences in matching persistence behavior during block design performance between older normal and brain-damaged persons: A process analysis. Cortex, 10(2), 121–132. doi: 10.1016/S0010-9452(74)80003-1 Google Scholar
Benedict, R.H., & Zgaljardic, D.J. (1998). Practice effects during repeated administrations of memory tests with and without alternate forms. Journal of Clinical and Experimental Neuropsychology, 20(3), 339–352. doi: 10.1076/jcen.20.3.339.822.CrossRefGoogle ScholarPubMed
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi: 10.1037/0033-2909.107.2.238 Google Scholar
Blair, C.K., Folsom, A.R., Knopman, D.S., Bray, M.S., Mosley, T.H., & Boerwinkle, E., … Atherosclerosis Risk in Communities (ARIC) Study Investigators. (2005). APOE genotype and cognitive decline in a middle-aged cohort. Neurology, 64(2), 268–276. doi: 10.1212/01.WNL.0000149643.91367.8A CrossRefGoogle Scholar
Bontempo, D.E., & Hofer, S.M. (2007). Assessing factorial invariance in cross-sectional and longitudinal studies. In A.D. Ong & M. van Dulmen (Eds.), Handbook of methods in positive psychology (pp. 153–175). New York: Oxford University Press.Google Scholar
Borenstein, A.R., Copenhaver, C.I., & Mortimer, J.A. (2006). Early-life risk factors for Alzheimer disease. Alzheimer Disease and Associated Disorders, 20(1), 63–72. doi: 10.1097/01.wad.0000201854.62116.d7 CrossRefGoogle ScholarPubMed
Borsboom, D., Romeijn, J.W., & Wicherts, J.M. (2008). Measurement invariance versus selection invariance: Is fair selection possible? Psychological Methods, 13, 75–98. doi: 10.1037/1082-989X.13.2.75 Google Scholar
Boyle, P.A., Wilson, R.S., Aggarwal, N.T., Tang, Y., & Bennett, D.A. (2006). Mild cognitive impairment: Risk of Alzheimer disease and rate of cognitive decline. Neurology, 67(3), 441–445.CrossRefGoogle ScholarPubMed
Burke, E.F. (1997). A short note on the persistence of retest effects on aptitude scores. Journal of Occupational and Organizational Psychology, 70, 295–301.CrossRefGoogle Scholar
Buschke, H. (1973). Selective reminding for analysis of memory and learning. Journal of Verbal Learning and Verbal Behavior, 12, 543–550. doi: 10.1016/S0022-5371(73)80034-9 Google Scholar
Cagney, K.A., & Lauderdale, D.S. (2002). Education, wealth, and cognitive functioning in later life. Journal of Gerontology: Psychological Sciences, 2, 163–172. doi: 10.1093/geronb/57.2.P163 Google Scholar
Calamia, M., Markon, K., & Tranel, D. (2012). Scoring higher the second time around: Meta-analyses of practice effects in neuropsychological assessment. The Clinical Neuropsychologist, 26(4), 543–570. doi: 10.1080/13854046.2012.680913 Google Scholar
Collie, A., Maruff, P., Darby, D.G., & McStephen, M. (2003). The effects of practice on the cognitive test performance of neurologically normal individuals assessed at brief test-retest intervals. Journal of the International Neuropsychological Society, 9(3), 419–428.Google Scholar
Cooper, D.B., Lacritz, L.H., Weiner, M.F., Rosenberg, R.N., & Cullum, C.M. (2004). Category fluency in mild cognitive impairment: Reduced effect of practice in test–retest conditions. Alzheimer Disease and Associated Disorders, 18, 120–122.Google Scholar
Darby, D., Maruff, P., Collie, A., & McStephen, M. (2002). Mild cognitive impairment can be detected by multiple assessments in a single day. Neurology, 59, 1042–1046. doi: 10.1212/WNL.59.7.1042 Google Scholar
Del Ser, T., Gonzalez-Montalvo, J.-I., MartinezEspinosa, S., Delgado-Villapalos, C., & Bermejo, F. (1997). Estimation of premorbid intelligence in Spanish people with the Word Accentuation Test and its application to the diagnosis of dementia. Brain and Cognition, 33, 343–356.Google Scholar
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test. (2nd ed.), San Antonio, TX: Psychological Corporation.Google Scholar
Dodge, H.H., Wang, C.N., Chang, C.C., & Ganguli, M. (2011). Terminal decline and practice effects in older adults without dementia: The MoVIES project. Neurology, 77(8), 722–730. doi: 10.1212/WNL.0b013e31822b0068 CrossRefGoogle ScholarPubMed
Dodrill, C.B., & Troupin, A.S. (1975). Effects of repeated administrations of a comprehensive neuropsychological battery among chronic epileptics. The Journal of Nervous and Mental Disease, 161(3), 185–190. doi: 10.1097/00005053-197509000-00006 Google Scholar
Duff, K., Lyketsos, C.G., Beglinger, L.J., Chelune, G., Moser, D.J., Arndt, S., & McCaffrey, R.J. (2011). Practice effects predict cognitive outcome in amnestic mild cognitive impairment. American Journal of Geriatric Psychiatry, 19(11), 932–939. doi: 10.1097/JGP.0b013e318209dd3a Google Scholar
Dugbartey, A.T., Townes, B.D., & Mahurin, R.K. (2000). Equivalence of the Color Trails Test and Trail Making Test in nonnative English-speakers. Archives of Clinical Neuropsychology, 15(5), 425–431. doi: 10.1016/S0887-6177(99)00034-7 Google Scholar
Ferrer, E., Salthouse, T.A., McArdle, J.J., Stewart, W.F., & Schwartz, B.S. (2005). Multivariate modeling of age and retest in longitudinal studies of cognitive abilities. Psychology and Aging, 20(3), 412–422. doi: 10.1037/0882-7974.20.3.412 Google Scholar
Ferrer, E., Salthouse, T.A., Stewart, W.F., & Schwartz, B.S. (2004). Modeling age and retest processes in longitudinal studies of cognitive abilities. Psychology and Aging, 19(2), 243–259. doi: 10.1037/0882-7974.19.2.243 Google Scholar
Flicker, L. (2010). Cardiovascular risk factors, cerebrovascular disease burden, and healthy brain aging. Clinics in Geriatric Medicine, 26(1), 17–27. doi: 10.1016/j.cger.2009.12.005 Google Scholar
Frank, R., Wiederholt, W.C., Kritz-Silverstein, D.K., Salmon, D.P., & Barrett-Connor, E. (1996). Effects of sequential neuropsychological testing of an elderly community-based sample. Neuroepidemiology, 15(5), 257–268. doi: 10.1159/000109915 Google Scholar
Gibbons, L.E., Carle, A.C., Mackin, R.S., Harvey, D., Mukherjee, S., & Insel, P., … Alzheimer’s Disease Neuroimaging Initiative. (2012). A composite score for executive functioning, validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment. Brain Imaging and Behavior, 6(4), 517–527.Google Scholar
Glymour, M.M., & Manly, J.J. (2008). Lifecourse social conditions and racial and ethnic patterns of cognitive aging. Neuropsychology Review, 18, 223–254. doi: 10.1007/s11065-008-9064-z Google Scholar
Glymour, M.M., Weuve, J., Berkman, L.F., Kawachi, I., & Robins, J.M. (2005). When is baseline adjustment useful in analyses of change? An example with education and cognitive change. American Journal of Epidemiology, 162(3), 267–278.Google Scholar
Gould, S.J. (1996). The mismeasure of man. New York: W.W. Norton & Company.Google Scholar
Gross, A.L., Jones, R.N., Fong, T.G., Tommet, D., & Inouye, S.K. (2014). Calibration and validation of an innovative approach for estimating general cognitive performance. Neuroepidemiology, 42, 144–153.CrossRefGoogle ScholarPubMed
Gross, A.L., Sherva, R., Mukherjee, S., Newhouse, S., Kauwe, J.S.K., & Munsie, L.M., … AD Genetics Consortium. (2014). Calibrating longitudinal cognition in Alzheimer’s disease across diverse test batteries and datasets. Neuroepidemiology, 43, 194–205.Google Scholar
Haan, M.N., Shemanski, L., Jagust, W.J., Manolio, T.A., & Kuller, L. (1999). The role of APOE epsilon4 in modulating effects of other risk factors for cognitive decline in elderly persons. Journal of the American Medical Association, 282, 40–46. doi: 10.1001/jama.282.1.40 Google Scholar
Harrison, J.E., Buxton, P., Husain, M., & Wise, R. (2000). Short test of semantic and phonological fluency: Normal performance, validity and test-retest reliability. British Journal of Clinical Psychology, 39(Pt 2), 181–191. doi: 10.1348/014466500163202 CrossRefGoogle ScholarPubMed
Hausknecht, J.P., Halpert, J.A., Di Paolo, N.T., & Gerrard, M.O.M. (2007). Retesting in selection: A meta-analysis of coaching and practice effects for tests of cognitive ability. Journal of Applied Psychology, 92, 373–385.CrossRefGoogle ScholarPubMed
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(2), 128–137. doi: 10.1097/WAD.0b013e3181ffa76d Google Scholar
Heilbronner, R.L., Sweet, J.J., Attix, D.K., Krull, K.R., Henry, G.K., & Hart, R.P. (2010). Official position of the American Academy of Clinical Neuropsychology on serial neuropsychological assessments: The utility and challenges of repeat test administrations in clinical and forensic contexts. The Clinical Neuropsychologist, 24, 1267–1278. doi:10.1080/13854046.2010.526785. doi: 10.1080/13854046.2010.526785 Google Scholar
Hernan, M.A., & Robins, J.M. (2006). Estimating causal effects from epidemiological data. Journal of Epidemiology and Community Health, 60, 578–596. doi: 10.1136/jech.2004.029496 Google Scholar
Hoffman, L., Hofer, S.M., & Sliwinski, M.J. (2011). On the confounds among retest gains and age-cohort differences in the estimation of within-person change in longitudinal studies: A simulation study. Psychology and Aging, 26, 778–791. doi: 10.1037/a0023910 Google Scholar
Hoffmeyer-Zlotnik, J.H.P., & Warner, U. (2005). How to measure education in cross-national comparison: Hoffmeyer-Zlotnik/Warner-Matrix of education as a new instrument. In J.H.P. Hoffmeyer-Zlotnik & J.A. Harkness (Eds.), Methodological aspects in cross-national research (pp. 223–240). ZUMA Nachrichten Special 11. Mannheim: ZUMA.Google Scholar
Horton, A.M. Jr. (1992). Neuropsychological practice effects x age: A brief note. Perceptual and Motor Skills, 75(1), 257–258.Google Scholar
Howieson, D., Carlson, N., Moore, M., Wasserman, D., Abendroth, C., Payne-Murphy, J., & Kaye, J. (2008). Trajectory of mild cognitive impairment onset. Journal of the International Neuropsychological Society, 14, 192–198.Google Scholar
Ivnik, R.J., Smith, G.E., Lucas, J.A., Petersen, R.C., Boeve, B.F., Kokmen, E., & Tangalos, E.G. (1999). Testing normal older people three or four times at 1- to 2-year intervals: Defining normal variance. Neuropsychology, 13, 121–127.Google Scholar
Jacqmin-Gadda, H., Fabrigoule, C., Commenges, D., & Dartigues, J.F. (1997). A five-year longitudinal study of Mini-Mental State Examination in normal aging. American Journal of Epidemiology, 145, 498–506.CrossRefGoogle Scholar
Jacobs, D.M., Sano, M., Dooneief, G., Marder, K., Bell, K.L., & Stern, Y. (1995). Neuropsychological detection and characterization of preclinical Alzheimer's disease. Neurology, 45(5), 957–962. doi: 10.1212/WNL.45.5.957 Google Scholar
Johnson, J.K., Gross, A.L., Pa, J., McLaren, D.G., Park, L.Q., & Manly, J.J., for the Alzheimer’s Disease Neuroimaging Initiative (2012). Longitudinal change in neuropsychological performance using latent growth models: A study of mild cognitive impairment. Brain Imaging and Behavior, 6(4), 540–550. doi: 10.1007/s11682-012-9161-8 Google Scholar
Jones, R.N., Rudolph, J.L., Inouye, S.K., Yang, F.M., Fong, T.G., Milberg, W.P., & Marcantonio, E.R. (2010). Development of a unidimensional composite measure of neuropsychological functioning in older cardiac surgery patients with good measurement precision. Journal of Clinical and Experimental Neuropsychology, 32, 1041–1049.Google Scholar
Juster, F.T., & Suzman, R. (1995). An overview of the Health and Retirement Study. Journal of Human Resources, 30(Suppl.), 7–56. doi: 10.2307/146277 Google Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (1983). The Boston Naming Test. Philidelphia: Lea and Febiger.Google Scholar
Kausler, D.H. (1994). Learning and memory in normal aging. San Diego, CA: Academic Press.Google Scholar
Laird, N.M., & Ware, J.H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. doi: 10.2307/2529876.Google Scholar
Langa, K.M., Plassman, B.L., Wallace, R.B., Herzog, A.R., Heeringa, S.G., Ofstedal, M.B., & Willis, R.J. (2005). The Aging, Demographics, and Memory Study: Study design and methods. Neuroepidemiology, 25, 181–191. doi: 10.1159/000087448 Google Scholar
Little, R.J.A., & Rubin, D.B. (1987). Statistical analysis with missing data. New York: John Wiley & Sons.Google Scholar
Luchsinger, J.A., Tang, M.-X., Shea, S., & Mayeux, R. (2001). Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort. American Journal of Epidemiology, 154, 635–641. doi: 10.1093/aje/154.7.635 Google Scholar
Luchsinger, J.A., Reitz, C., Honig, L.S., Tang, M.-X., Shea, S., & Mayeux, R. (2005). Aggregation of vascular risk factors and risk of incident Alzheimer’s disease. Neurology, 65, 545–551. doi: 10.1212/01.wnl.0000172914.08967.dc CrossRefGoogle Scholar
Machulda, M.M., Pankratz, V.S., Christianson, T.J., Ivnik, R.J., Mielke, M.M., Roberts, R.O., & Petersen, R.C. (2013). Practice effects and longitudinal cognitive change in normal aging vs. incident mild cognitive impairment and dementia in the Mayo Clinic Study of Aging. The Clinical Neuropsychologist, 27(8), 1247–1264. doi: 10.1080/13854046.2013.836567 Google Scholar
Manly, J.J., Byrd, D.A., Touradji, P., & Stern, Y. (2004). Acculturation, reading level, and neuropsychological test performance among African American elders. Applied Neuropsychology, 11(1), 37–46.Google Scholar
Manly, J.J., Jacobs, D.M., Sano, M., Bell, K., Merchant, C.A., ... Stern, Y. (1999). Effect of literacy on neuropsychological test performance in nondemented, education-matched elders. Journal of the International Neuropsychological Society, 5(3), 191–202.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(3), 341–348.Google Scholar
Manly, J.J., Schupf, N., Tang, M.-X., & Stern, Y. (2005). Cognitive decline and literacy among ethnically diverse elders. Journal of Geriatric Psychiatry and Neurology, 18, 213–217. doi: 10.1177/0891988705281868 CrossRefGoogle ScholarPubMed
Manly, J.J., Tang, M.X., Schupf, N., Stern, Y., Vonsattel, J.P., & Mayeux, R. (2008). Frequency and course of mild cognitive impairment in a multiethnic community. Annals of Neurology, 63(4), 494–506.Google Scholar
Manly, J.J., Touradji, P., Tang, M.X., & Stern, Y. (2003). Literacy and memory decline among ethnically diverse elders. Journal of Clinical and Experimental Neuropsychology, 25(5), 680–690.Google Scholar
Mann, V.A., Sasanuma, S., Sakuma, N., & Masaki, S. (1990). Sex differences in cognitive abilities: A cross-cultural perspective. Neuropsychologia, 28(10), 1063–1077. doi: 10.1016/0028-3932(90)90141-A Google Scholar
Mattis, S. (1988). Dementia Rating Scale: Professional manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
McCaffrey, R.J., Onega, A., Orsillo, S.M., Nelles, W.B., & Haase, R.F. (1992). Practice effects in repeated neuropsychological assessments. The Clinical Neuropsychologist, 6, 32–42. doi: 10.1080/13854049208404115 Google Scholar
Meyer, D.E., & Schvaneveldt, R.W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227–234. doi: 10.1037/h0031564 Google Scholar
Mitrushina, M., Boone, K.B., Razani, J., & D’Elia, L.F. (2005). Handbook of normative data for neuropsychological assessment (2nd ed.). New York, NY: Oxford University Press.Google Scholar
Mitrushina, M., & Satz, P. (1991). Effect of repeated administration of a neuropsychological battery in the elderly. Journal of Clinical Psychology, 47(6), 790–801. doi: 10.1002/1097-4679(199111)47:6<790::AID-JCLP2270470610>3.0.CO;2-C3.0.CO;2-C>CrossRefGoogle ScholarPubMed
Mungas, D., Reed, B.R., Marshall, S.C., & González, H.M. (2000). Development of psychometrically matched English and Spanish language neuropsychological tests for older persons. Neuropsychology, 14(2), 209–223.Google Scholar
Muthén, B.O., & Curran, P.J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371–402. doi: 10.1037//1082-989X.2.4.371 Google Scholar
Muthén, L.K., & Muthén, B.O. (1998–2012). Mplus User's Guide (7th ed.), Los Angeles, CA: Muthén & Muthén.Google Scholar
Perani, D., Bressi, S., Cappa, S.F., Vallar, G., Alberini, M., Grassi, F., & Fazio, F. (1993). Evidence of multiple memory systems in the human brain: A [18F] FDG PET metabolic study. Brain, 116, 903–919. doi: 10.1093/brain/116.4.903 Google Scholar
Rabbitt, P.M. (1993). Does it all go together when it goes? Quarterly Journal of Experimental Psychology, 46(A), 385–433. doi: 10.1080/14640749308401055 CrossRefGoogle ScholarPubMed
Rabbitt, P., Diggle, P., Smith, D., Holland, F., & McInnes, L. (2001). Identifying and separating the effects of practice and of cognitive ageing during a large longitudinal study of elderly community residents. Neuropsychologia, 39(5), 532–543. doi: 10.1016/S0028-3932(00)00099-3 Google Scholar
Rabbitt, P., Diggle, P., Holland, F., & McInnes, L. (2004). Practice and drop-out effects during a 17-year longitudinal study of cognitive aging. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 59(2), 84–97. doi: 10.1093/geronb/59.2.P84 CrossRefGoogle ScholarPubMed
Rabbitt, P., Lunn, M., Wong, D., & Cobain, M. (2008). Age and ability affect practice gains in longitudinal studies of cognitive change. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 63(4), P235–P240. doi: 10.1093/geronb/63.4.P235 Google Scholar
Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.), Thousand Oaks, CA: Sage.Google Scholar
Reitan, R. (1958). Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276. doi: 10.2466/PMS.8.7.271-276 CrossRefGoogle Scholar
Ronnlund, M., & Nilsson, L.G. (2006). Adult life-span patterns in WAIS-R Block Design performance: Cross-sectional versus longitudinal age gradients and relations to demographic factors. Intelligence, 34, 63–78. doi:10.1016/j.intell.2005.06.004 Google Scholar
Ronnlund, M., Nyberg, L., Backman, L., & Nilsson, L. (2005). Stability, growth, and decline in adult life span development of declarative memory: Cross-sectional and longitudinal data from a population-based study. Psychology and Aging, 20, 3–18. doi: 10.1037/0882-7974.20.1.3 Google Scholar
Sabe, L., Jason, L., Juejati, M., Leiguarda, R., & Starkstein, S.E. (1995). Dissociation between declarative and procedural learning in dementia and depression. Journal of Clinical and Experimental Neuropsychology, 17, 841–848. doi: 10.1080/01688639508402433 Google Scholar
Salthouse, T.A. (2009). When does age-related cognitive decline begin? Neurobiology and Aging, 30(4), 507–514. doi: 10.1016/j.neurobiolaging.2008.09.023 Google Scholar
Salthouse, T.A. (2010a). Major issues in cognitive aging. New York: Oxford University Press.Google Scholar
Salthouse, T.A. (2010b). Influence of age on practice effects in longitudinal neurocognitive change. Neuropsychology, 24, 563–572. doi: 10.1037/a0019026 Google Scholar
Salthouse, T., Schroeder, D., & Ferrer, E. (2004). Estimating retest effects in longitudinal assessments of cognitive functioning in adults between 18 and 60 years of age. Developmental Psychology, 40(5), 813–822. doi: 10.1037/0012-1649.40.5.813 Google Scholar
Salthouse, T.A., & Tucker-Drob, E.M. (2008). Implications of short-term retest effects for the interpretation of longitudinal change. Neuropsychology, 22, 800–811. doi: 10.1037/a0013091 CrossRefGoogle ScholarPubMed
Schaie, K.W. (2005). Developmental influences on adult intelligence: The Seattle Longitudinal Study. New York: Oxford University Press.Google Scholar
Schneider, B.C., Gross, A.L., Bangen, K.J., Skinner, J.C., Benitez, A., Glymour, M.M., & Luchsinger, J.A. (2014). Association of vascular risk factors with cognition in a multiethnic sample. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 70(4), 532–544.Google Scholar
Schofield, P.W., Mosesson, R., Stern, Y., & Mayeux, R. (1995). The age at onset of Alzheimer’s disease and an intracranial area measurement: A relationship. Arch Neurol, 52(1), 95–98.Google Scholar
Schrijnemaekers, A.M., de Jager, C.A., Hogervorst, E., & Budge, M.M. (2006). Cases with mild cognitive impairment and Alzheimer’s disease fail to benefit from repeated exposure to episodic memory tests as compared with controls. Journal of Clinical and Experimental Neuropsychology, 28, 438–455. doi: 10.1080/13803390590935462 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(3), 402–411. doi: 10.1037/a0017515 Google Scholar
Singer, J.D., & Willet, J. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.Google Scholar
Sliwinski, M., Hoffman, L., & Hofer, S. (2010). Modeling retest and aging effects in a measurement burst design. In P.C.M. Molenaar & K.M. Newell (Eds.), Individual pathways of change: Statistical models for analyzing learning and development (pp 37–50). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Spreen, O., & Benton, A. (1969). Neurosensory Centre Comprehensive Examination for Aphasia. Victoria, British Columbia, Canada: University of Victoria.Google Scholar
Steiger, J.H., & Lind, J.C. (1980). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society; Iowa City, IA.Google Scholar
Stern, Y., Andrews, H., Pittman, J., Sano, M., Tatemichi, T., Lantigua, R., & Mayeux, R. (1992). Diagnosis of dementia in a heterogeneous population. Development of a neuropsychological paradigm-based diagnosis of dementia and quantified correction for the effects of education. Archives of Neurology, 49, 453–460.Google Scholar
Stern, Y., Gurland, B., Tatemichi, T.K., Tang, M.X., Wilder, D., & Mayeux, R. (1994). Influence of education and occupation on the incidence of Alzheimer’s disease. Journal of the American Medical Association, 271, 1004–1010. doi: jama.271.13.1004 Google Scholar
Stuss, D., Stethem, L., & Poirier, C. (1987). Comparison of three tests of attention and rapid information processing across six age groups. The Clinical Neuropsychologist, 1, 139–152.Google Scholar
Tang, M.-X., Cross, P., Andrews, H., Jacobs, D.M., Small, S., Bell, K., & Mayeux, R. (2001). Incidence of AD in African-Americans, Caribbean Hispanics, and Caucasians in northern Manhattan. Neurology, 56, 49–56. doi: 10.1212/WNL.56.1.49 Google Scholar
Tang, M.X., Stern, Y., Marder, K., Bell, K., Gurland, B., Lantiqua, R., & Mayeux, R. (1998). The APOE-epsilon4 allele and the risk of Alzheimer disease among African Americans, whites, and Hispanics. Journal of the American Medical Association, 279(10), 751–755. doi: 10.1001/jama.279.10.751 Google Scholar
Thorndike, E.L. (1922). Practice effects in intelligence tests. Journal of Experimental Psychology, 5, 101–107. doi: 10.1037/h0074568 Google Scholar
Tulving, E., & Markowitsch, H.J. (1998). Episodic and declarative memory: Role of the hippocampus. Hippocampus, 8(3), 198–204. doi: 10.1002/(SICI)1098-1063(1998)8:3<198::AID-HIPO2>3.3.CO;2-JGoogle Scholar
Van der Elst, W., Van Boxtel, M.P.J., Van Breukelen, G.J.P., & Jolles, J. (2008). Detecting the significance of changes in performance on the Stroop Color-Word Test, Rey’s Verbal Learning Test, and the Letter Digit Substitution Test: The regression-based change approach. Journal of the International Neuropsychological Society, 14, 71–80. doi: 10.10170S1355617708080028 CrossRefGoogle ScholarPubMed
Voyer, D., Voyer, S., & Bryden, M.P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250–270. doi: 10.1037//0033-2909.117.2.250 Google Scholar
Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised. New York: The Psychological Corporation.Google Scholar
Wilkinson, G.S., & Robertson, G.J. (2006). Wide Range Achievement Test 4 professional manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
Wilson, R.S., Li, Y., Bienias, J.L., & Bennett, D.A. (2006). Cognitive decline in old age: Separating retest effects from the effects of growing older. Psychology and Aging, 21(4), 774–789. doi: 10.1037/0882-7974.21.4.774 Google Scholar
Wilson, R., Leurgans, S., Boyle, P., & Bennett, B. (2011). Cognitive decline in prodromal Alzheimer disease and mild cognitive impairment. Archives of Neurology, 68, 351–356.Google Scholar
Zehnder, A., Blasi, S., Berres, M., Spiegel, R., & Monsch, A. (2007). Lack of practice effects on neuropsychological tests as early cognitive markers of Alzheimer disease? American Journal of Alzheimer’s Disease and Other Dementias, 22, 416–426.Google Scholar
Supplementary material: File

Gross supplementary material

Appendix Tables 1-3

Download Gross supplementary material(File)
File 58.1 KB