Behavior Genetics

, Volume 33, Issue 3, pp 221–246 | Cite as

Phenotypic and Behavioral Genetic Covariation Between Elemental Cognitive Components and Scholastic Measures

  • Dasen Luo
  • Lee Anne Thompson
  • Douglas K. Detterman
Article

Abstract

The study subjected nine elementary cognitive task variables from the Cognitive Assessment Tasks (CAT) and three scholastic measures from the Metropolitan Achievement Test (MAT) to phenotypic and behavioral genetic structural equation modeling based on data for 277 pairs of same sex monozygotic (MZ) and dizygotic (DZ) twins from the Western Reserve Twin Project. Phenotypic and behavioral genetic covariation between certain elemental cognitive components and scholastic performance was examined to determine (a) whether these elemental cognitive components contribute substantially to the variance of scholastic performance; (b) whether such contributions vary across different domains of school knowledge or from specific domains to a general aptitude; (c) the behavioral genetic composition of the elemental cognitive components and the scholastic variables; and (d) how the association between the cognitive components and scholastic performance is genetically and environmentally mediated. The results of the study showed that as much as 30% of the phenotypic variance of scholastic performance was accounted for by the CAT general factor, which was presumably related to mental speed. A mainly genetic covariation was found between the mental speed component and scholastic performance, although each of the two variables was strongly influenced by both heritability and common family environment. The magnitude and etiology of the covariation were largely invariant whether mental speed was related to a common scholastic aptitude or to individual achievement measures covering different knowledge domains. Taken in conjunction with previous findings that mental speed has a substantial genetic correlation with psychometric g, and psychometric g has a mostly genetic covariation with scholastic achievement, the findings of the present study seems to point to a more global picture; namely, there is a causal sequence that starts from mental speed as the explanatory factor for both psychometric g and scholastic performance, and the etiology of the causal link is chiefly genetic.

Behavioral genetic mediation twins scholastic performance elementary cognitive tasks mental speed structural equation modeling intelligence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, L. A., Vernon, P. A., and Ho, H.-Z. (1991). The genetic correlation between intelligence and speed of information processing. Behav. Genet. 21:351-367.Google Scholar
  2. Bentler, P. M., and Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88:588-606.Google Scholar
  3. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychol. Bull. 107:238-246.Google Scholar
  4. Brooks, A., Fulker, D. W., and DeFries, J. C. (1990). Reading performance and general cognitive ability: A multivariate genetic analysis of twin data. Personality Individual Differences 11:141-146.Google Scholar
  5. Cardon, L. R., DiLalla, L. F., Plomin, R., DeFries, J. C., and Fulker, D. W. (1990). Genetic correlations between reading performance and IQ in the Colorado Adoption Project. Intelligence 4:245-257.Google Scholar
  6. Cardon, L. R., Fulker, D. W., DeFries, J. C., and Plomin, R. (1992). Multivariate genetic analysis of specific cognitive abilities in the Colorado Adoption Project at age 7. Intelligence 16:383-400.Google Scholar
  7. Ceci, S. J. (1991). How much does schooling influence intellectual development and its cognitive components? A reassessment of the evidence. Dev. Psych. 27:703-722.Google Scholar
  8. Ceci, S. J. (1992). Schooling and intelligence. Psychol. Sci. Agenda 5:7-9.Google Scholar
  9. Deary, I. J. (1980). How general is the mental speed factor in “general” intelligence: An attempt to extend inspection time to the auditory modality. B.S. honors thesis, University of Edinburgh, Scotland.Google Scholar
  10. Deary, I. J. (1986). Inspection time: Discovery or rediscovery? Personality Individual Differences 7:625-632.Google Scholar
  11. Detterman, D. K. (1990). Manual for the Cognitive Assessment Tasks battery. Technica Manual.Google Scholar
  12. Freason, W. M., and Eysenck, J. J. (1986). Intelligence, reaction time (RT) and a new “odd-man-out” RT paradigm. Personality Individual Differences 7:807-817.Google Scholar
  13. Fraser, C., and McDonald, R. P. (1988). COSAN: Covariance structure analysis. Multivariate Behav. Res. 23:263-265.Google Scholar
  14. Ho, H.-Z., Baker, L. A., and Decker, S. N. (1988). Covariance between intelligence and speed of cognitive processing: Genetic and environmental influences. Behav. Genet. 18:247-261.Google Scholar
  15. Jensen, A. R. (1982a). Reaction time and psychometric g. In J. J., Eysenck (ed.), A model for intelligence (pp. 93-132). New York: Springer.Google Scholar
  16. Jensen, A. R. (1982b). The chronometry of intelligence. In R. J., Sternberg (ed.), Advances in the psychology of human intelligence, Vol. 1 (pp. 255-310). Hillsdale, NJ: Eribaum.Google Scholar
  17. Jensen, A. R. (1987). Individual differences in the Hick paradigm. In P. A., Vernon (ed.), Speed of information-processing and intelligence. 18 Norwood, NJ: Ablex.Google Scholar
  18. Jensen, A. R. (1998). The g factor. Westport, CT: Praeger Publishers.Google Scholar
  19. Krane, W. R., and McDonald, R. P., (1978). Scale invariance and the factor analysis of correlation matrices. Br. J. Math. Stat. Psychol. 31:217-228.Google Scholar
  20. Lange, K., Westlake, J., and Spence, M. A. (1976). Extensions to pedigree analysis. III. Variance components by the scoring method. Ann. Hum. Genet. 39:485-491.Google Scholar
  21. Luo, D., and Petrill, S. (1999). Elementary cognitive tasks and their roles in g estimates. Intelligence 27(2):157-174.Google Scholar
  22. Marsh, H. W., Balla, J. R., and McDonald, R. P. (1988). Goodnessof-fit indexes in confirmatory factor analysis: The effect of sample size. Psychol. Bull. 103:391-410.Google Scholar
  23. Miller, L. T., and Vernon, P. A. (1992). The general factor in short term memory, intelligence, and reaction time. Intelligence 16:5-30.Google Scholar
  24. Nettlebeck, T., and Lally, M. (1976). Inspection time and measured intelligence. Br. J. Psychol. 67:17-22.Google Scholar
  25. Neale, M. C. (1991). MX: Statistical modeling. Richmond, VA: Department of Human Genetics.Google Scholar
  26. Nichols, R. C., and Bilbro, W. C. (1966). The diagnosis of twin zygosity. Acta Genet. 16:265-275.Google Scholar
  27. Raz, N., Willerman, L., Lgmundson, P., and Hanlon, M. (1983). Aptitude-related differences in auditory recognition masking. Intelligence 7:71-90.Google Scholar
  28. Raz, N., and Willerman, L. (1985). Aptitude-related differences in auditory information processing: Effects of selective attention and tone duration. Personality Individual Differences 6:299-304.Google Scholar
  29. Schmid, J., and Leiman, J. (1957). The development of hierarchical factor solutions. Psychometrika 22:53-61.Google Scholar
  30. Steiger, J. H., and Lind, J. C. (1980). Statistically based tests for the number of common factors. Article presented at the annual meeting of the Psychometric Society, Iowa City, IA.Google Scholar
  31. Thompson, L. A., Detterman, D. K., and Plomin, R. (1991). Associations between cognitive abilities and scholastic achievement: Genetic overlap but environmental differences. Psychol. Sci. 2:158-165.Google Scholar
  32. Tucker, L. R., and Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika 38:1-10.Google Scholar
  33. Vernon, P. A. (1987). Speed of information processing and intelligence. Norwood, NJ: Ablex.Google Scholar
  34. Wechsler, H. S. (1977). The qualified student. New York: John Wiley and Sons.Google Scholar

Copyright information

© Plenum Publishing Corporation 2003

Authors and Affiliations

  • Dasen Luo
  • Lee Anne Thompson
  • Douglas K. Detterman

There are no affiliations available

Personalised recommendations