General cognitive ability assessment in the German National Cohort (NAKO) – The block-adaptive number series task

References

• Adams RJ. 2005. Reliability as a measurement design effect. Stud Educ Eval. 31(2–3):162–172.
   Google Scholar
• Allaire JC, Marsiske M. 1999. Everyday cognition: age and intellectual ability correlates. Psychol Aging. 14(4):627–644.
   PubMed Web of Science ®Google Scholar
• Angrisani M, Kapteyn A, Lusardi A. 2016. The national financial capability study: empirical findings from the American Life Panel survey. FINRA Report.
   Google Scholar
• Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. J Stat Softw. 67(1):1–48.
   Web of Science ®Google Scholar
• Batty GD, Deary IJ, Gottfredson LS. 2007. Premorbid (early life) IQ and later mortality risk: systematic review. Ann Epidemiol. 17(4):278–288.
   PubMed Web of Science ®Google Scholar
• Berger K, Rietschel M, Rujescu D. 2022. The value of ‘mega cohorts’ for psychiatric research. World J Biol Psychiatry. DOI:10.1080/15622975.2021.2011405
   Web of Science ®Google Scholar
• Bertram L, Böckenhoff A, Demuth I, Düzel S, Eckardt R, Li SC, Lindenberger U, Müller L, Pawelec G, Siedler T, et al. 2014. Cohort profile: The Berlin Aging Study II (BASE-II). Int J Epidemiol. 43(3):703–712.
   PubMed Web of Science ®Google Scholar
• Calvin CM, Batty GD, Der G, Brett CE, Taylor A, Pattie A, Čukić I, Deary IJ. 2017. Childhood intelligence in relation to major causes of death in 68 year follow-up: prospective population study. BMJ. 357:j2708.
   PubMedGoogle Scholar
• Calvin CM, Deary IJ, Fenton C, Roberts BA, Der G, Leckenby N, Batty GD. 2011. Intelligence in youth and all-cause-mortality: systematic review with meta-analysis. Int J Epidemiol. 40(3):626–644.
   PubMed Web of Science ®Google Scholar
• Carroll JB. 1993. Human cognitive abilities. Cambridge: Cambridge University Press.
   Google Scholar
• Christensen GT, Mortensen EL, Christensen K, Osler M. 2016. Intelligence in young adulthood and cause-specific mortality in the Danish Conscription Database – A cohort study of 728,160 men. Intelligence. 59:64–71.
   Web of Science ®Google Scholar
• Cohen J. 1988. Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale (NJ): Lawrence Erlbaum Associates, Publishers.
   Google Scholar
• Conway ARA, Kane MJ, Engle RW. 2003. Working memory capacity and its relation to general intelligence. Trends Cogn Sci. 7(12):547–552.
   PubMed Web of Science ®Google Scholar
• Davies NM, Hill WD, Anderson EL, Sanderson E, Deary IJ, Smith GD. 2019. Multivariable two-sample mendelian randomization estimates of the effects of intelligence and education on health. Elife. 8:8.
   Web of Science ®Google Scholar
• Deary IJ, Batty GD. 2007. Cognitive epidemiology. J Epidemiol Community Health. 61(5):378–384.
   PubMed Web of Science ®Google Scholar
• Deary IJ, Strand S, Smith P, Fernandes C. 2007. Intelligence and educational achieve- ment. Intelligence. 35(1):13–21.
   Web of Science ®Google Scholar
• Dragano N, Reuter M, Greiser KH, Becher H, Zeeb H, Mikolajczyk R, Kluttig A, Leitzmann M, Fischer B, Jöckel K-H, et al. 2020. Socio-demographic and employment-related factors in the German National Cohort (GNC; NAKO Gesundheitsstudie). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 63(3):267–278.
   PubMed Web of Science ®Google Scholar
• Embretson SE, Reise SP. 2000. Item response theory (1st ed.). New York: Psychology Press.
   Google Scholar
• Engelhardt L, Goldhammer F. 2018. Number Series Study (DIPF): Technical Report. (GESIS Papers, 2018/01). Köln: GESIS – Leibniz-Institut für Sozialwissenschaften.
   Google Scholar
• Engelhardt L, Goldhammer F. 2019. Validating Test Score Interpretations Using Time Information. Front Psychol. 10:1131.
   PubMed Web of Science ®Google Scholar
• Erhardt A, Gelbrich G, Klinger-König J, Streit F, Kleineidam L, Riedel-Heller SG, Schmidt B, Schmiedek F, Wagner M, Grabe HJ, et al. 2021. Generalized anxiety and panic symptoms in the German National Cohort (NAKO). Submitted to. World J Biol Psychiatry. 2021:1–37.
   Google Scholar
• Fawns-Ritchie C, Starr JM, Deary IJ. 2018. Role of cognitive ability in the association between functional health literacy and mortality in the Lothian Birth Cohort 1936: a prospective cohort study. BMJ Open. 8(9):e022502.
   PubMed Web of Science ®Google Scholar
• Fisher GG, McArdle JJ, McCammon RJ, Sonnega A, Weir DR. 2013. New measures of fluid intelligence in the HRS, HRS Documentation Report DR-027. Survey Research Center, University of Michigan.
   Google Scholar
• Flynn JR. 1984. The mean IQ of Americans: massive gains from 1932 to 1978. Psychol Bull. 95(1):29–51.
   Web of Science ®Google Scholar
• Friedman NP, Miyake A, Corley RP, Young SE, DeFries JC, Hewitt JK. 2006. Not all executive functions are related to intelligence. Psychol Sci. 17(2):172–1789.
   PubMed Web of Science ®Google Scholar
• German National Cohort (NAKO) Consortium. 2014. The German National Cohort: aims, study design and organization. Eur J Epidemiol. 29:371–382.
   PubMed Web of Science ®Google Scholar
• Gershon RC. 2005. Computer adaptive testing. J Appl Meas. 6(1):109–127.
   PubMedGoogle Scholar
• Goldhammer F. 2015. Measuring ability, speed, or both? Challenges, psychometric solutions, and what can be gained from experimental control. Measurement. 13(3–4):133–164.
   Web of Science ®Google Scholar
• Gustafsson J-E. 1984. A unifying model for the structure of intelligence. Intelligence. 8(3):179–203.
   Web of Science ®Google Scholar
• International Standard Classification of Education, ISCED 1997. 2003. In: Advances in Cross-National Comparison.
   Google Scholar
• Jaffe LE. 2009. Development, interpretation, and application of the W score and the relative proficiency index (Woodcock-Johnson III Assessment Service Bulletin No. 11). Rolling Meadows (IL): Riverside Publishing.
   Google Scholar
• Jäger AO, Süß H-M, Beauducel A, 1997. Der Berliner Intelligenzstruktur-Test (BIS-Test; Form 4). Test manual [The Berlin Intelligence Structure Test (BIS Test; Form 4): Test manual]. Göttingen: Hogrefe.
   Google Scholar
• Kleineidam L, Stark M, Riedel-Heller S, Pabst A, Schmiedek F, Streit F, Rietschel M, Klinger-König J, Grabe HJ, Erhardt A, et al. 2022. The assessment of cognitive functions in the German National Cohort (NAKO) – Associations of demographics and psychiatric symptoms on test performance. World J Biol Psychiatry. doi:10.1080/15622975.2021.2011408
   Web of Science ®Google Scholar
• Klinger-König J, Streit F, Erhardt A, Kleineidam L, Schmiedek F, Schmidt B, Peters A, Fischer B, Leitzmann M, Kaaks R, et al. 2022. The assessment of childhood maltreatment and its associations with affective symptoms in adulthood: results of the German National Cohort (NAKO). World J Biol Psychiatry. doi:10.1080/15622975.2021.2011406
   Google Scholar
• Li S-C, Lindenberger U, Hommel B, Aschersleben G, Prinz W, Baltes PB. 2004. Transformations in the couplings among intellectual abilities and constituent cognitive processes across the life span. Psychol Sci. 15(3):155–163.
   PubMed Web of Science ®Google Scholar
• Linacre JM. 2012. What do infit and outfit, mean-square and standardized mean? Rasch Meas Transactions. 16(2):878.
   Google Scholar
• Lindberg SM, Hyde JS, Petersen JL, Linn MC. 2010. New trends in gender and mathematics performance: a meta-analysis. Psychol Bull. 136(6):1123–1135.
   PubMed Web of Science ®Google Scholar
• Lindenberger U, Singer T, Baltes PB. 2002. Longitudinal selectivity in aging populations: separating mortality-associated versus experimental components in the Berlin Aging Study (BASE). J Gerontol B-Psychol. 57B(6):474–482.
   Google Scholar
• Little TD, Lindenberger U, Nesselroade JR. 1999. On selecting indicators for multivariate measurement and modeling with latent variables: when “good” indicators are bad and “bad” indicators are good. Psychol Meth. 4(2):192–211.
   Web of Science ®Google Scholar
• Lövdén M, Fratiglioni L, Glymour MM, Lindenberger U, Tucker-Drob EM. 2020. Education and cognitive functioning across the life span. Psychol Sci Public Interest. 21(1):6–41.
   PubMed Web of Science ®Google Scholar
• Marshalek B, Lohman DF, Snow RE. 1983. The complexity continuum in the radex and hierarchical models of intelligence. Intelligence. 7(2):107–127.
   Web of Science ®Google Scholar
• McArdle JJ, Ferrer-Caja E, Hamagami F, Woodcock R. 2002. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Dev Psychol. 38(1):115–142.
   PubMed Web of Science ®Google Scholar
• McArdle JJ. 2013. Adaptive testing of the number series test using standard approaches and a new decision tree analysis approach. In: McArdle JJ, Ritschard G. Contemporary issues in exploratory data mining in the behavioral sciences. New York: Routledge; p. 312–342.
   Google Scholar
• Muthén L, Muthén B. 2007. Mplus User’s Guide (Version 7).
   Google Scholar
• Pollard MS. Baird MD 2017. The RAND American Life Panel: Technical Description. Santa Monica, CA: RAND Corporation. https://www.rand.org/pubs/research_reports/RR1651.html.
   Google Scholar
• Prescott C, Walters EE, McArdle JJ, Lapham SJ. 2018. The project talent twin & sibling study: understanding effects of family rearing environment on cognitive abilities. Innov Aging. 2(suppl_1):859–859.
   Google Scholar
• Prindle JJ. 2012. A functional use of response time data in cognitive assessment. Dissertation. University of Southern California. http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll3/id/19203.
   Google Scholar
• R Core Team. 2017. R: A language and environment for statistical computing. Vienna, Austria. [accessed 2020 Dec 1]. https://www.R-project.org/.
   Google Scholar
• Rasch G. 1960. Probabilistic models for some intelligence and attainment tests. Chicago: University of Chicago Press.
   Google Scholar
• Rey-Mermet A, Gade M, Souza AS, von Bastian CC, Oberauer K. 2019. Is executive control related to working memory capacity and fluid intelligence? J Exp Psychol Gen. 148(8):1335–1372.
   PubMed Web of Science ®Google Scholar
• Ritchie SJ, Tucker-Drob EM. 2018. How much does education improve intelligence? A meta-analysis. Psychol Sci. 29(8):1358–1369.
   PubMed Web of Science ®Google Scholar
• Robitzsch R, Kiefer T, Wu M. 2018. TAM: Test analysis modules. https://CRAN.R-project.org/package=TAM.
   Google Scholar
• Salthouse T. 2019. Trajectories of normal cognitive aging. Psychol Aging. 34(1):17–24.
   PubMed Web of Science ®Google Scholar
• Schaie KW. 1994. The course of adult intellectual development. Am Psychol. 49(4):304–313.
   PubMed Web of Science ®Google Scholar
• Schmidt FL, Hunter JE. 1998. The validity and utility of selection methods in personnel psychology: practical and theoretical implications of 85 years of research findings. Psychol Bull. 124(2):262–274.
   Web of Science ®Google Scholar
• Streit F, Zillich L, Frank J, Kleineidam L, Wagner M, Baune BT, Klinger-König J, Grabe HJ, Pabst A, Riedel-Heller SG, et al. 2021. Lifetime and current depression in the German National Cohort (NAKO).
   Google Scholar
• Strenze T. 2007. Intelligence and socioeconomic success: a meta-analytic review of longitudinal research. Intelligence. 35(5):401–426.
   Web of Science ®Google Scholar
• Sundet JM, Barlaug DG, Torjussen TM. 2004. The end of the Flynn effect? A study of secular trends in mean intelligence test scores of Norwegian conscripts during half a century. Intelligence. 32(4):349–362.
   Web of Science ®Google Scholar
• Van der Linden WJ. 2018. Handbook of item response theory. Boca Raton (FL): CRC Press.
   Google Scholar
• Verhaeghen P, Salthouse T. 1997. Meta-analyses of age-cognition relations in adulthood: estimates of linear and nonlinear age effects and structural models. Psychol Bull. 122(3):231–249.
   PubMed Web of Science ®Google Scholar
• Wilhelm O, Schulze R. 2002. The relation of speeded and unspeeded reasoning with mental speed. Intelligence. 30(6):537–554.
   Web of Science ®Google Scholar
• Wood SN. 2017. Generalized additive models: An introduction with R. Boca Raton: Chapman and Hall.
   Google Scholar
• Woodcock RW, Dahl MN. 1971. A common scale for the measurement of person ability and test item difficulty (AGS Paper No. 10). Circle Pines (MN): American Guidance Service.
   Google Scholar
• Woodcock RW, Johnson MB. 1977. Woodcock-Johnson Psycho-Educational Battery. Allen (TX): DLM.
   Google Scholar
• Woodcock RW, McGrew KS, Mather N. 2001. Woodcock Johnson III. Rolling Meadows (IL): Riverside.
   Google Scholar
• Wraw C, Deary IJ, Gale CR, Der G. 2015. Intelligence in youth and health at age 50. Intelligence. 53:23–32.
   PubMed Web of Science ®Google Scholar
• Wraw C, Der G, Gale CR, Deary IJ. 2018. Intelligence in youth and health behaviours in middle age. Intelligence. 69:71–86.
   PubMed Web of Science ®Google Scholar
• Yan D, von Davier AA, Lewis C. 2014. Computerized multistage testing: theory and applications. Raton (FL): CRC Press.
   Google Scholar