Structural Models of Developmental Theory in Psychology

  • J. J. McArdle
Part of the Annals of Theoretical Psychology book series (AOTP, volume 7)


This is a response to the presentation by Wohlwill (this volume). To begin, I must admit that I have been a follower of Wohlwill’s research for a long time. In particular my own research has benefited from Wohlwill’s classic work on The age variable in psychological research (see Wohlwill, 1970, 1973). His current paper adds clarity and force to these issues so here I continue my enthusiastic support of Wohlwill’s work.


Factor Invariance Latent Growth Curve Latent Growth Model Dynamic Factor Model Focus Model 
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  1. Arminger, G. (1986). Linear stochastic differential equation models for panel data with unobserved variables. In N. B. Tuma (Ed.), Sociological Methodology, 16, 187–213.Google Scholar
  2. Baltes, P. B. (1987). Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline. Developmental Psychology, 23(5), 611–626.CrossRefGoogle Scholar
  3. Baltes, P. B., Reese, H. W., & Nesselroade, J. R. (1988). Introduction to research methods: Life span development psychology. Hillsdale, NJ: Erlbaum.Google Scholar
  4. Blalock, H. M. (1985). Causal models in panel and experimental designs. New York: Aldine Publishers.Google Scholar
  5. Cattell, R. B. (1966). Handbook of multivariate experimental psychology. New York: Rand McNally.Google Scholar
  6. Cattell, R. B. (1982). Personality and learning theory. New York: Rand McNally.Google Scholar
  7. Featherman, D. L., & Peterson, T. (1986). Markers of aging: Modeling the clocks that time us. Research on Aging 8(3), 339–365).PubMedCrossRefGoogle Scholar
  8. Geweke, J., & Singleton, K. (1981). Maximum likelihood confirmatory factor analysis of economic time series. International Economic Review, 22, 37–54.CrossRefGoogle Scholar
  9. Griffiths, D., & Sandland, R. (1984). Fitting generalized allometric models to multivariate growth data. Biometrics, 40, 139–150.CrossRefGoogle Scholar
  10. Horn, J. L. (1972). State, trait and change dimensions of intelligence. The British Journal of Educational Psychology, 42(2), 159–185.CrossRefGoogle Scholar
  11. Horn, J. L., McArdle, J. J., & Mason, R. (1983). When is invariance not invariant: A practical scientist’s look at the ethereal concept of factor invariance. The Southern Psychologist, 1(4), 179–188.Google Scholar
  12. Humphreys, M. S., & Revelle, W. (1984). Personality, motivation and performance: A theory of the relationship between individual differences and information processing. Psychological Review, 91(2), 153–184.PubMedCrossRefGoogle Scholar
  13. Jones, M. B. (1962). Practice as a process of simplification. Psychological Review, 69(4), 274–294.PubMedCrossRefGoogle Scholar
  14. Jöreskog, K. G. (1970). Estimation and testing of simplex models. British Journal of Mathematical and Statistical Psychology, 23, 121–146.CrossRefGoogle Scholar
  15. Jöreskog, K. G., & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.Google Scholar
  16. Kearsley, G. P., Buss, A. R., & Royce, J. R. (1977). Developmental change and the multidimensional cognitive system. Intelligence, 1, 257–273.CrossRefGoogle Scholar
  17. Keats, J. A. (1983). Ability measures and theories of cognitive development. In H. Wainer & S. Messick (Eds.), Principals of modern psychological measurement: A festschrift for Frederic M. Lord (pp. 81–101). Hillsdale, NJ: Erlbaum.Google Scholar
  18. Kessler, R. G, & Greenberg, D. F. (1981). Linear panel analysis: Models of quantitative change. New York: Academic Press.Google Scholar
  19. Loehlin, J. C. (1987). Latent variable models: An introduction to factor, path, and structural analysis. Hillsdale, NJ: Erlbaum.Google Scholar
  20. May, R. M. (1981). Theoretical ecology: principles and applications. Sunderland, MA: Sinauer Associates.Google Scholar
  21. McArdle, J. J. (1986) Latent variable growth within behavior genetic models Behavior Genetics, 16(1), 163–200.PubMedCrossRefGoogle Scholar
  22. McArdle, J. J. (1988a). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), The handbook of multivariate experimental psychology (Vol. 2. pp. 561–614). New York: Plenum Press.CrossRefGoogle Scholar
  23. McArdle, J. J. (1988b). Structural modeling experiments using multiple growth curves. In P. Ackerman, R. Kanfer, & R. Cudeck (Eds.), Learning and individual differences: Abilities, motivation, and methodology. Hillsdale, NJ: Erlbaum.Google Scholar
  24. McArdle, J. J., Anderson, E., & Aber, M. S. (1987). Convergence hypotheses modeled and tested with linear structural equations. Proceedings of the National Center for Health Statistics Conference (pp. 351–357), NCHS, Hyattsville, MD.Google Scholar
  25. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58(1), 110–133.PubMedCrossRefGoogle Scholar
  26. McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale, NJ: Erlbaum.Google Scholar
  27. Meredith, W., & Tisak, J. (1984). Tuckerizing curves. Paper presented at the Annual Meetings of the Psychometric Society, Santa Barbara, CA (submitted for publication.Google Scholar
  28. Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50(2), 181–202.CrossRefGoogle Scholar
  29. Neimark, E. D., & Estes, W. K. (1967). Stimulus sampling theory. San Francisco, CA: Holden-Day.Google Scholar
  30. Nesselroade, J. R. (1983). Temporal selection and factor invariance in the study of development and change. Life-span Development & Behavior, 5, 59–87.Google Scholar
  31. Nesselroade, J. R., & Ford, D. (1985). P-technique comes of age. Multivariate, replicated, single-subject designs for research on older subjects. Research on Aging, 7, 46–80.PubMedCrossRefGoogle Scholar
  32. Newtson, D., Hairfield, J., Bloomingdale, J., & Cutino, S. (1987). The structure of action and interaction. Social Cognition, 5(3), 197–237.CrossRefGoogle Scholar
  33. Ramsey, J. O. (1982). When the data are functions. Psychometrika, 47(4), 379–389.CrossRefGoogle Scholar
  34. Rogosa, D., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrica, 50(2), 203–228.CrossRefGoogle Scholar
  35. Rozeboom, W. (1978). General linear dynamic analysis (GLDA). Department of Psychology, University of Alberta, Edmonton, Canada.Google Scholar
  36. Tucker, L. R. (1966). Learning theory and multivariate experiment: Illustration by determination of parameters of generalized learning curves. In R. B. Cattell (Ed.), The handbook of multivariate experimental psychology (pp. 476–501). Chicago: Rand McNally.Google Scholar
  37. Waber, D. P., Mann, M. B., Merola, J., & Moylan, P. M. (1985). Physical maturation rate and cognitive performance in early adolescence: A longitudinal examination. Developmental Psychology, 21(4), 666–681.CrossRefGoogle Scholar
  38. Wohlwill, J. F. (1970). The age variable in psychological research. Psychological Review, 77(1), 49–64.CrossRefGoogle Scholar
  39. Wohlwill, J. F. (1973). The study of behavioral development. New York: Academic Press.Google Scholar
  40. Woodbury, M. A., & Manton, K. G. (1983). A mathematical model of physiological dynamics of aging and correlated selection. I. Theoretical development and critiques. Journal of Gerontology, 38(4), 398–405.PubMedCrossRefGoogle Scholar
  41. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.Google Scholar
  42. Zeaman, D., & House, B. J. (1963). The role of attention in retardate discrimination learning. In N. R. Ellis (Ed.), Handbook of mental deficiency (pp. 155–223). New York: McGraw-Hill.Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • J. J. McArdle
    • 1
  1. 1.Department of PsychologyUniversity of VirginiaCharlottesvilleUSA

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