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On estimating true change interrelationships with other variables

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Abstract

This article is concerned with the estimation of indexes of interrelationship between true change in repeatedly assessed latent constructs and other variables. In the social and behavioral sciences this theoretically and empirically important issue arises commonly in research aimed at studying correlates and predictors of growth or decline in a context of a longitudinal research design. A structural equation modeling approach is described that is useful in empirical situations where identifying variables correlated with change in longitudinally followed latent constructs is of interest. The issue of discerning between correlates and predictors of change using the structural equation modeling methodology is discussed. The described approach is used to study correlative aspects of ability growth in a cognitive intervention study (Balteset al., 1986).

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References

  • Alwin, D. F. & Jackson, D. J. (1980). Measurement models for response errors in surveys. In K. Schuessler (Ed.),Sociological Methodology (pp. 68–119). San Francisco: Jossey Bass.

    Google Scholar 

  • Baltes, P. B. & Willis, S. L. (1982). Plasticity and enhancement of intellectual functioning in old age: Penn State's adult development and enrichment project (ADEPT). In F. I. M. Craik & S. Trehub (Eds.),Aging and Cognitive Processes (pp. 353–389). New York: Plenum.

    Google Scholar 

  • Baltes, P. B., Dittmann-Kohli, F., & Kliegl, R. (1986). Reserve capacity of the elderly in agingsensitive tests of fluid intelligence: Replication and extension.Psychology and Aging 1, 172–177.

    Google Scholar 

  • Belsky, J. (1990).The Psychology of Aging: Theory, Research, and Interventions. Pacific Grove, CA: Brooks/Cole.

    Google Scholar 

  • Bentler, P. M. (1978). The interdependence of theory, methodology, and empirical data. In D. B. Kandel (Ed.),Longitudinal Drug Research (pp. 267–302). New York: Wiley.

    Google Scholar 

  • Bentler, P. M. (1989).EQS: Structural Equation Program Manual. Los Angeles, CA: BMDP Statistical Software.

    Google Scholar 

  • Blieszener, R., Willis, S. L., & Baltes, P. B. (1981). Training research in aging on the fluid ability of inductive reasoning.Journal of Applied Developmental Psychology 2, 247–265.

    Google Scholar 

  • Breckler, S. (1990). Applications of covariance structure modeling in psychology: Cause for concern?Psychological Bulletin 107, 260–273.

    Google Scholar 

  • Browne, M. W. & Mels, G. (1990).RAMONA PC: User's Guide. Columbus, OH: Department of Psychology, Ohio State University.

    Google Scholar 

  • Cavanaugh, J. C. (1990).Adult Development and Aging. Belmont, CA: Wadsworth.

    Google Scholar 

  • Cochran, W. G. (1968). Errors of measurement in statistics.Technometrics 10, 637–666.

    Google Scholar 

  • Cronbach, L. J. & Furby, L. (1970). How should we “measure” change — or should we?Psychological Bulletin 74, 68–80.

    Google Scholar 

  • Duncan, O. D. (1969). Some linear models for two-wave, two-variables panel analysis.Psychological Bulletin 72, 177–182.

    Google Scholar 

  • Duncan, O. D. (1975).Introduction to Structural Equation Models. New York: Academic Press.

    Google Scholar 

  • Ekström, R. B., French, J. W., Harman, J., & Derman, D. (1976).Kit of Factor-Referenced Cognitive Tests. Princeton, NJ: Educational Testing Service.

    Google Scholar 

  • Fraser, C. (1992).COSAN: User's Guide.

  • Hertzog, C. & Schaie, K. W. (1986). Stability and change in adult intelligence 1.: Analysis of longitudinal covariance structures.Psychology and Aging 1, 159–171.

    Google Scholar 

  • Hertzog, C. & Schaie, K. W. (1988). Stability and change in adult intelligence 2.: Simultaneous analysis of longitudinal means and covariance structures.Psychology and Aging 3, 122–130.

    Google Scholar 

  • Huba, G. J. & Bentler, P. M. (1982). On the usefulness of latent variable causal modeling in testing theories of naturally occurring events: A rejoinder to Martin.Journal of Personality and Social Psychology 43, 304–311.

    Google Scholar 

  • Isaac, P. D. (1970). Linear regression, structural relations, and measurement error.Psychological Bulletin 74, 213–218.

    Google Scholar 

  • Joreskog, K. G. & Sorbom, D. (1988).LISREL 7: A Guide to the Program and its Applications. Chicago, IL: SPSS Scientific Software.

    Google Scholar 

  • Kendall, M. G. & Stuart, A. S. (1962).The Advanced Theory of Statistics, Vol. 2. London: Griffin.

    Google Scholar 

  • Kessler, R. C. & Greenberg, D. F. (1981).Linear Panel Analysis: Models of Quantitative Change. New York: Academic Press.

    Google Scholar 

  • Lacey, J. I. & Lacey, B. C. (1962). The law of initial values in the longitudinal study of autonomic constitution. In W. M. Wolf (Ed.),Rhythmic Functions in the Living Systems. Annals of the New York Academy of Sciences 28, 1257–1290.

  • Lee, S. (1987).Model Equivalence in Covariance Structure Modeling. Doctoral Dissertation (unpublished). Columbus, OH, Ohio State University.

    Google Scholar 

  • Lee, S. & Hershberger, S. (1991). A simple rule for generating equivalent models in covariance structure modeling.Multivariate Behavioral Research 25, 313–334.

    Google Scholar 

  • Linn, R. L. & Slinde, J. A. (1977). The determination of the significance of change between pre- and posttesting periods.Review of Educational Research 49, 121–150.

    Google Scholar 

  • Lord, F. M. (1960). Large-sample covariance analysis when the control variable is fallible.Journal of the American Statistical Association 55, 307–321.

    Google Scholar 

  • Lord, F. M. & Novick, R. (1968).Statistical Theories of Mental Test Scores. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Luijben, T. C. W. (1991). Equivalent models in covariance structure analysis.Psychometrika 56, 653–665.

    Google Scholar 

  • Manning, W. H. & DuBois, P. H. (1962). Correlational methods in research on human learning.Perceptual and Motor Skills 15, 287–321.

    Google Scholar 

  • Muthen, B. (1987).Analysis of Linear Structural Equations with a Comprehensive Measurement Model: Theoretical Integration and User's Guide. Mooresville, IN: Scientific Software.

    Google Scholar 

  • Raykov, T. (1989). Reserve capacity of the elderly in aging sensitive tasks of fluid intelligence: A reanalysis via a structural equation modeling approach.Zeitschrift fur Psychologie 197, 123–132.

    Google Scholar 

  • Raykov, T. (1992a). Structural models for studying correlates and predictors of change.Australian Journal for Psychology 44, 101–112.

    Google Scholar 

  • Raykov, T. (1992b). On structural models for analyzing change.Scandinavian Journal of Psychology 33, 247–265.

    Google Scholar 

  • Raykov, T. (1993b). Classical test theory based and factor analytic structural models for analyzing change: A note on differences and similarities.Scandinavian Journal of Psychology 34, 94–96.

    Google Scholar 

  • Raykov, T. (1993b). A structural equation model for measuring residualized change and discerning patterns of growth or decline.Applied Psychological Measurement, 17 (in press).

  • Raykov, T., Tomer, A. & Nesselroade, J. R. (1991). Reporting structural equation modeling results inPsychology and Aging: Some proposed guidelines.Psychology and Aging 6, 499–503.

    Google Scholar 

  • Rogosa, D. R. (1980). Critique of cross-lagged correlations.Psychological Bulletin 88, 121–132.

    Google Scholar 

  • Rogosa, D. R., Brandt, D. & Zimowski, M. (1982). A growth curve approach to the measurement of change.Psychological Bulletin 90, 726–748.

    Google Scholar 

  • Rogosa, D. R. & Willett, J. (1983). Demonstrating the reliability of the difference score in the measurement of change.Journal of Educational Measurement 20, 335–343.

    Google Scholar 

  • Rogosa, D. R. & Willett, J. B. (1985). Understanding correlate of change by modeling individual differences in growth.Psychometrika 50, 203–228.

    Google Scholar 

  • SAS Institute (1990). SAS technical report P-200. SAS/STAT Software: CALIS and LOGISTIC procedures. Cary, NC: SAS Institute.

    Google Scholar 

  • Schaie, K. W. & Hertzog, C. (1985). Measurement in the psychology of adulthood and aging. In J. E. Birren and K. W. Schaie (Eds.),Handbook of the Psychology of Aging (Second Edition, pp. 61–92). New York: Van Nostrand Reinhold.

    Google Scholar 

  • Steiger, J. H. (1989).EzPATH: A supplementary module for SYSTAT and SYGRAPH. Evanston, IL: SYSTAT.

    Google Scholar 

  • Stelzl, I. (1986). Changing a causal hypothesis without changing the fit: Some rules for generating equivalent path models.Multivariate Behavioral Research 21, 309–331.

    Google Scholar 

  • Tucker, L. R., Damarin, F., & Messick, S. (1966). A base-free measure of change.Psychometrika 31, 457–473.

    Google Scholar 

  • Woodruff-Pack, D. S. (1988).Psychology and Aging. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Zimmerman, D. W. & Williams, R. H. (1982a). The relative error magnitude in three measures of change.Psychometrika 47, 141–147.

    Google Scholar 

  • Zimmerman, D. W. & Williams, R. H. (1982b). Gain scores in research can be highly reliable.Journal of Educational Measurement 19, 149–153.

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Psychology, University of Melbourne, 3052, Parkville, VIC, Australia

    Tenko Raykov

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  1. Tenko Raykov
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Raykov, T. On estimating true change interrelationships with other variables. Qual Quant 27, 353–370 (1993). https://doi.org/10.1007/BF01102498

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