Behavior Genetics

, Volume 20, Issue 5, pp 569–608 | Cite as

Alternative common factor models for multivariate biometric analyses

  • J. J. McArdle
  • H. H. Goldsmith
Article
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Abstract

In prior research we have shown how linear structural equation models and computer programs (e.g., LISREL) may be simply and directly used to provide alternatives for the traditional biometric twin design. We use structural equations and path models to define biometric group differences, we write traditional common-factor models in the same way, and then we take a detailed look at some alternative multivariate and biometric models. We contrast the biometricfactors covariance structure approach used by Loehlin and Vandenberg (1968), Martin and Eaves (1977), and others with the psychometric-factors approach used by McArdle et al. (1980) and others. We use the multivariate primary mental abilities data on monozygotic (MZ) and dizygotic (DZ) twins from Loehlin and Vandenberg (1968) to detail fundamental differences in model specification and results. We extend both multivariate biometric approaches using exploratory and confirmatory multiple-factor models. These comparisons show that each alternative multivariate methodology has useful features for empirical applications.

Key Words

twins multivariate factor analysis structural equation models LISREL RAM intelligence primary mental abilities 
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References

  1. Behrman, J., Taubman, P., and Wales, T. (1977). Controlling for and measuring the effects of genetics and family environment in equations for schooling and labor market success. In Taubman, P. (ed.),Kinometrics: Determinants of Socioeconomic Success Within and Between Families, North-Holland, Amsterdam.Google Scholar
  2. Bock, R. D. (1989).Multilevel Analysis of Educational Data, Academic Press, San Diego.Google Scholar
  3. Bock, R. D., and Vandenberg, S. G. (1968). Components of heritable variation in mental test scores, In Vandenberg, S. G. (ed.),Progress in Human Behavior Genetics, Johns Hopkins Press, Baltimore.Google Scholar
  4. Boomsma, D. I., and Gabrielli, W. F. (1985). Behavioral genetic approaches to psychophysiology data.Psychophysiology 22:249–260.PubMedGoogle Scholar
  5. Boomsma, D. I., and Molenaar, P. C. M. (1986). Using LISREL to analyze genetic and environmental covariance structure.Behav. Genet. 16: 237–250.Google Scholar
  6. Cantor, R. M., Nance, W. E., Eaves, L. J., Winter, P. M., and Blanchard, M. M. (1983). Analysis of the covariance structure of digital ridge counts in the offspring of monozygotic twins.Genetics 103:495–512.PubMedGoogle Scholar
  7. Carey, G. (1986). A general multivariate approach to linear modeling in human genetics.Am. J. Hum. Genet. 39:775–786.PubMedGoogle Scholar
  8. Cattell, R. B. (1960). The multiple abstract variance analysis equations and solutions: For nature-nuture research on continoous variables.Psychol. Rev. 67:353–372.PubMedGoogle Scholar
  9. Cattell, R. B. (1982).The Inheritance of Personality and Ability: Research Methods and Findings, Academic Press, New York.Google Scholar
  10. Chamberlain, G., and Grilliches, Z. (1977). More on brothers. In Taubman, P. (ed.),Kinometrics: Determinants of Socioeconomic Success Within and Between Families, North-Holland, Amsterdam.Google Scholar
  11. Crawford, C. B., and DeFries, J. C. (1978). Factor analysis of genetic and environmental correlation matrices.Multivar. Behav. Res. 13:297–318.Google Scholar
  12. DeFries, J. C. (1979). Comment on Fulker's “Some implications of biometrical genetical analysis for psychological research,” In Royce, J. R., and Mos, L. P. (eds.),Theoretical Advances in Behavior Genetics, Sijthoff & Noordhoff, Alphen aan den Rijn, The Netherlands, pp. 381–383.Google Scholar
  13. Eaves, L. J., Martin, N. B., and Eysenck, S. B. G. (1977). An application of the analysis of covariance structures to the psychogenetical study of impulsiveness.Br. J. Math. Stat. Psychol. 30:1–42.Google Scholar
  14. Eaves, L. J., Last, K. A., Young, P. A., and Martin, N. G. (1978). Model-fitting approaches to the analysis of human behavior.Heredity 41:249–320.PubMedGoogle Scholar
  15. Fraser, C. (1979).COSAN User's Guide, Ontarior Institute for Studies in Education, Toronto (Note: Center for Behavioral Studies, University of New England, Armidale, N.S.W., Australia).Google Scholar
  16. Fulker, D. W. (1978). Multivariate extensions of a biometrical model of twin data. In Nance, W. E., et al., (eds.),Twin Research: Psychology and Methodology, Liss, New York, pp. 217–236.Google Scholar
  17. Fulker, D. W. (1979). Some implications of biometrical genetical analysis for psychological research. In Royce, J. R., Mos, L. P. (eds.),Theoretical Advances in Behavior Genetics, Sitjhoff & Noordhoff, Alphen aan den Rijn, The Netherlands.Google Scholar
  18. Fulker, D. W., Baker, L. A., and Bock, R. D. (1983). Estimating components of covariance using LISREL.Data Anal. Commun. Comp. Data Anal. 14:261–281.Google Scholar
  19. Goldberger, A. S. (1978). Pitfalls in the resolution of intelligence inheritance. In Morton, N. E., and Chang, C. S. (eds.),Genetic Epidemiology, Academic Press,New York, pp. 30–45.Google Scholar
  20. Goldsmith, H. H. (1984). Continuity of personality: A genetic perspective.In Emde, R. M., and Harmon, R. J. (eds.),Continuities and Discontinuities in Development,Plenum, New York.Google Scholar
  21. Goldsmith, H. H. (1983). Genetic influences on personality from infancy to adulthood.Child Dev. 54:331–355.PubMedGoogle Scholar
  22. Henderson, N. D. (1982). Human behavior genetics.Annu. Rev. Psychol. 33:403–440.PubMedGoogle Scholar
  23. Horn, J. L. (1986). Intellectual ability concepts. In Sternberg, R. J. (ed.),Advances in the Psychology of Human Intelligence, Erlbaum, Hillsdale, N. J.Google Scholar
  24. Horn, J. L., and McArdle, J. J. (1980). Perspectives on mathematical and statistical model building (MASMOB) in aging research. In Poon, L. W. (ed.),Aging in the 1980s: Contemporary Perspectives, American Psychological Association, Washington, D.C.Google Scholar
  25. Jaspars, J. M. F., and deLeeuw, J. A. (1980). Genetic-environment covariation in human behaviour genetics. In van der Kamp, L. J. Th., Langerak, W. F., and de Gruijter, D. N. M. (eds.),Psychometrics for Educational Debates, Wiley, New York.Google Scholar
  26. Jinks, J. L., and Fulker, D. W. (1970). Comparison of the biometrical genetical, MAVA, and classical approaches to the analysis of human behavior.Psychol. Bull.73:311–349.PubMedGoogle Scholar
  27. Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis.Psychometrika 34:183–202.Google Scholar
  28. Jöreskog, K. G. (1970). A general model for the analysis of covariance structures.Biometrika 57:239–251.Google Scholar
  29. Jöreskog, K. G., and Sörbom, D. (1979). In Magidson, J. (ed.),Advances in Factor Analysis and Structural Equation Models, Abt Books, Cambridge, Mass.Google Scholar
  30. Kempthorne, O., and Osborne, R. H. (1961). The interpretation of twin data.Am.J. Hum. Genet. 13:320–329.PubMedGoogle Scholar
  31. Lange, K., and Boehnke, M. (1983). Extensions to pedigree analysis. IV. Covariance component models for multivariate traits.Am. J. Med. Genet. 14:513–524.PubMedGoogle Scholar
  32. Loehlin, J. C. (1987).Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, Erlbaum, Hillsdale, N.J.Google Scholar
  33. Loehlin, J. C. (1978). Heredity-environment analyses of Jenck's IQ correlations.Behav. Genet. 8:(5):415–435.PubMedGoogle Scholar
  34. Loehlin, J. C. (1979). Combining data from different groups in human behavior genetics. In Royce, J. R., and Mos, L. P. (eds.),Theoretical Advances in Behavior Genetics, Sijthoff & Noordhoff, Germantown, Md.Google Scholar
  35. Loehlin, J. C., and Vandenberg, S. G. (1968). Genetic and environmental components in the covariation of cognitive abilities: An additive model. In Vandenberg, S. G. (ed.),Progress in Human Behavior Genetics, Johns Hopkins Press, Baltimore.Google Scholar
  36. Martin, N. G., and Eaves, L. G. (1977). The genetical analysis of covariance structure.Heredity 138:(1):79–95.Google Scholar
  37. Martin, N. G., Eaves, L. J., and Fulker, D. W. (1979). The genetical relationship of impulsiveness and sensation seeking to Eysenck's personality dimensions.Acta Genet. Med. Gemellol. 28:197–210.PubMedGoogle Scholar
  38. Martin, N. G., Jardine, R., and Eaves, L. J. (1984). Is there only one set of genes for different abilities.Behav. Genet. 14:355–370.Google Scholar
  39. Martin, N. G., Boomsma, D. I. and Neale, M. C. (1989). Forward.Behav. Genet. 19:5–7.Google Scholar
  40. McArdle, J. J. (1980). Causal modeling applied to psychonomic systems simulation.Behav. Res. Methods Instrument. 12:193–209.Google Scholar
  41. McArdle, J. J. (1986). Latent growth within behavior genetic models.Behav. Genet. 16:163–200.PubMedGoogle Scholar
  42. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In Nesselroade, J. R., and Cattell, R. B. (eds.),Handbook of Multivariate Experimental Psychology, Vol. II, Plenum Press, New York pp. 561–614.Google Scholar
  43. McArdle, J. J. and Boker, S. M. (1990),RAMpath: Computer software for path diagrams and path analysis. Denver, CO: Data Transforms.Google Scholar
  44. McArdle, J. J., and Goldsmith, H. H. (1984). Structural equation modeling applied to the twin design: Comparative multivariate models of the WAIS.Behav. Genet. 14:609 (abstract).Google Scholar
  45. McArdle, J. J., Goldsmith, H. H., and Horn, J. L. (1981). Genetic structural equation models of fluid and crystallized intelligence.Behav. Genet. 11:607 (abstract).Google Scholar
  46. McArdle, J. J., and Gottesman, I. I. (1987). Some models where independent ≠ different: A response to Plomin & Daniels.Behav. Brain Sci. 10(1):31–32.Google Scholar
  47. McArdle, J. J., and Horn, J. L. (1990). An effective graphic representation for linear structural equation models.Multivar. Behav. Res. (in press).Google Scholar
  48. McArdle, J. J., and McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures.Br. J. Math. Stat. Psychol. 37:234–251.PubMedGoogle Scholar
  49. McArdle, J. J., Connell, J. P., and Goldsmith, H. H. (1980). Structural modeling of stability and genetic influence: Some results from a longitudinal study of behavioral style.Behav. Genet. 10:487.Google Scholar
  50. McDonald, R. P. (1985).Factor Analysis and Related Methods, N.J. Erlbaum, Hillsdale.Google Scholar
  51. McDonald, R. P., and Krane, W. R. (1979). A Monte Carlo study of local identifiability and degrees of freedom in the asymptotic likelihood ratio test.Br. J. Math. Stat. Psychol 32:121–132.Google Scholar
  52. Meredith, W. (1968). Factor analysis and the use of inbred strains. In Vandenberg, S. G. (ed.),Progress in Human Behavior Genetics, Johns Hopkins Press, Baltimore.Google Scholar
  53. Neale, M. C. and McArdle, J. J. (1990). The analysis of assortative mating: A LISREL model.Behavior Genetics,20(2):287–293.PubMedGoogle Scholar
  54. Plomin, R., and Daniels, D. (1987) Why are children in the same family so different?Behav. Brain Sci. 10:37.Google Scholar
  55. Roudabush, G. (1968), Analyzing dyadic relationships. In Vandenberg, S. G. (ed.),Progress in Human Behavior Genetics, Johns Hopkins Press, Baltimore.Google Scholar
  56. Royce, J. R. (1979). The factor-gene basis of individuality. In Royce, J. R., and Mos, L. P. (eds.),Theoretical Advances in Behavior Genetics, Sijthoff & Noordhoff, Alphen aan den Rijn, The Netherlands.Google Scholar
  57. Spearman, D. (1904). General intelligence objectively determined and measured.Am. J. Psychol. 15:201–293.Google Scholar
  58. Tambs, K., Sundet, J. M., and Magnus, P. (1984). Heritability analysis of the WAIS subtests. A study of twins.Intelligence 8:283–293.Google Scholar
  59. Taubman, P. (1978). Determinants of socioeconomic success: Regression and latent variable analysis in samples of twins. In Nance, W. E., et al., (eds.),Twin Research: Psychology and Methodology, Liss, New York.Google Scholar
  60. Thurstone, L. L. (1947).Multiple Factor Analysis, University of Chicago Press, Chicago.Google Scholar
  61. Vandenberg, S. G. (ed.), (1968).Progress in Human Behavior Genetics: Recent Reports on Genetic Syndromes, Twin Studies, and Statistical Advances, Johns Hopkins Press, Baltimore.Google Scholar
  62. Wright, S. (1921). Correlation and causation.J. Agr. Res. 20:557–585.Google Scholar
  63. Wright, S. (1982). On “Path analysis in genetic epidemiology: A critique.”Am. J. Hum. Genet. 35:757–762.Google Scholar

Copyright information

© Plenum Publishing Corporation 1990

Authors and Affiliations

  • J. J. McArdle
    • 1
  • H. H. Goldsmith
    • 2
  1. 1.Department of PsychologyUniversity of VirginiaCharlottesville
  2. 2.Department of PsychologyUniversity of OregonEugene

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