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Abstract

The analysis of moment structures originated with the factor analysis model and with some simple pattern hypotheses concerning equality of elements of mean vectors and covariance matrices. They have more recently received considerable attention and been expanded to incorporate a variety of additional models. Covariance structures, some with associated mean structures, occur in psychology, economics, education, marketing, sociology, biometrics, and other disciplines. Most models involving covariance structures that are in current use are related to the factor analysis model in some way, either by being special cases with restrictions on parameters or, more commonly, extensions incorporating additional assumptions.

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References

  • Aitchison, J., and Silvey, S. D. (1960), “Maximum Likelihood Estimation Procedures and Associated Tests of Significance,” Journal of the Royal Statistical Society, Series B, 22, 154–171.

    Google Scholar 

  • Albert, A. A. (1944), “The Minimum Rank of a Correlation Matrix, ” Proceedings of the National Academy of Science, 30, 144–146.

    Article  Google Scholar 

  • Allison, P. D. (1987), “Estimation of Linear Models with Incomplete Data,” Sociological Methodology 1987, ed. C. C. Clogg, Washington, DC: American Sociological Association, 71–103.

    Google Scholar 

  • Amemiya, Y., and Anderson, T. W. (1990), “Asymptotic Chi-Square Test for a Large Family of Factor Analysis Models,” Annals of Statistics, 18, 1453–1463.

    Article  Google Scholar 

  • Anderson, T. W. (1960), “Some stochastic process models for intelligence test scores,” in: K. J. Arrow, S. Karlin, and P. Suppes (eds.), Mathematical Methods in the Social Sciences, pp. 205–220. Stanford: Stanford University Press.

    Google Scholar 

  • Anderson, T. W., and Amemiya, Y. (1988), “The Asymptotic Distribution of Estimators in Factor Analysis under General Conditions,”Annals of Statistics, 16, 759–771.

    Google Scholar 

  • Anderson, T. W., and Rubin, H. (1956), “Statistical Inference in Factor Analysis,” in J. Neyman (ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. V, Berkeley, University of California Press, 111–150.

    Google Scholar 

  • Arbuckle, J. (1994), AMOS 35: Analysis of Moment Structures,Chicago ILS mallwaters Corporation.

    Google Scholar 

  • Archer, C. O., and Jennrich, R. I. (1973), “Standard Errors for Orthogonally Rotated Factor Loadings,” Psychometrika, 38, 581–592.

    Article  Google Scholar 

  • Arminger, G. (1994), “Dynamic Factor Models for the Analysis of Ordered Categorical Panel Data,” unpublished manuscript, Department of Economics, University of Wuppertal.

    Google Scholar 

  • Arminger, G., and Schoenberg, R. J. (1989), “Pseudo maximum likelihood estimation and a test for misspecification in mean-and covariance-structure models,” Psychometrika, 54, 409–425.

    Article  Google Scholar 

  • Arminger, G., and Sobel, M. (1990), “Pseudo maximum likelihood estimation of mean and covariance structures with missing data,” Journal of the American Statistical Association, 85, 195–203.

    Article  Google Scholar 

  • Austin, J., and Wolfle, L. M. (1991), “Annotated bibliography of structural equation modeling: technical work,”British Journal of Mathematical and Statistical Psychology, 44, 93–152.

    Google Scholar 

  • Bagozzi, R. P., and Yi, Y. (1992), “Testing hypotheses about methods, traits and communalities in the direct product model,” Applied Psychological Measurement, 16, 373–380.

    Article  Google Scholar 

  • Bekker, P. A., Merckens, A., and Wansbeck, T. (1994), Identification, Equivalent Models and Computer Algebra, Boston: Academic Press.

    Google Scholar 

  • Bentler, P. M. (1989), Theory and Implementation of EQS, Los Angeles: BMDP Statistical Software Inc.

    Google Scholar 

  • Bentler, P. M. (1994), EQS4.0, BMDP Statistical Software, Los Angeles.

    Google Scholar 

  • Bentler, P. M., and Dijkstra, T. (1985), “Efficient Estimation via Linearization in Structural Models,” in: P. Krishnaiah (ed.), Multivariate Analysis VI, Amsterdam: Elsevier, 9–42.

    Google Scholar 

  • Bentler, P. M., and Weeks, D. G. (1980), “Linear Structural Equations with Latent Variables, ” Psychometrika, 45, 289–308.

    Article  Google Scholar 

  • Bock, R. D., and Gibbons, R. D. (1994), “High-dimensional Multivariate Probit Analysis,” unpublished manuscript, Department of Psychology, University of Chicago.

    Google Scholar 

  • Bollen, K. A. (1989), Structural Equations with Latent Variables, New York: Wiley. Bollen, K. A., and Long, J. S. (eds.) (1993), Testing Structural Equation Models, Newbury Park: Sage.

    Google Scholar 

  • Browne, M. W. (1974), “Generalised least squares estimators in the analysis of covariance structures,” South African Statistical Journal, 8, 1–24. [Reprinted in D.J. Aigner and A.S. Goldberger (eds.), Latent Variables in Socio-Economic Models, pp. 205–226. Amsterdam: North Holland, 1977.]

    Google Scholar 

  • Browne, M. W. (1982), “Covariance structures,” in D.M. Hawkins (ed.), Topics in Applied Multivariate Analysis, pp. 72–141, Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Browne, M. W. (1984a), “Asymptotically distribution free methods in the analysis of covariance structures,” British Journal of Mathematical and Statistical Psychology, 37, 62–83.

    Article  Google Scholar 

  • Browne, M. W. (1984b), “The decomposition of multitrait-multimethod matrices,” British Jour- nal of Mathematical and Statistical Psychology, 37, 1–21.

    Article  PubMed  Google Scholar 

  • Browne, M. W. (1990), “Asymptotic robustness of normal theory methods for the analysis of latent curves,” in W. Fuller and P. Brown (eds.) Statistical Analysis of Measurement Error Models and Applications, pp. 211–225, American Mathematical Society: Contemporary Mathematics Series, 112.

    Chapter  Google Scholar 

  • Browne, M. W. (1993), “Structured Latent Curve Models,” in C. M. Cuadras and C. R. Rao (eds.), Multivariate Analysis: Future Directions 2, pp. 171–198, Amsterdam: North Holland.

    Google Scholar 

  • Browne, M. W., and Cudeck, R. (1993), “Alternative ways of assessing model fit,” in: Bollen, K. A., and Long, J. S. (eds.), Testing Structural Equation Models, Newbury Park: Sage, 136–162.

    Google Scholar 

  • Browne, M. W., and Du Toit, S. H. C. (1991), “Models for learning data,” in L. M. Collins and J. Horn (eds.), Best Methods for the Analysis of Change, pp. 47–68, Washington: American Psychological Association.

    Google Scholar 

  • Browne, M. W., and Du Toit, S. H. C. (1992), “Automated fitting of nonstandard models,” Multivariate Behavioral Research, 27, 269–300.

    Article  Google Scholar 

  • Browne, M. W., and Mels, G. (1990), RAMONA PC User’s Guide. Report: Department of Statistics, University of South Africa.

    Google Scholar 

  • Browne, M. W., Mels, G., and Coward, M. (1994), “Path Analysis: Ramona,” SYSTAT for DOS: Advanced Applications, Version 6 Edition,Evanston, IL: Systat Inc., 163–224 (in press).

    Google Scholar 

  • Browne, M. W., and Shapiro, A. (1988), “Robustness of normal theory methods in the analysis of linear latent variate models,” British Journal of Mathematical and Statistical Psychology, 41, 193–208.

    Article  Google Scholar 

  • Cliff, N. (1983), “Some cautions concerning the application of causal modeling methods,” Multivariate Behavioral Research, 18, 115–126.

    Article  Google Scholar 

  • Cronbach, L. J., Gleser, G. C., Nanda, H., and Rajaratman, N. (1972), The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles, New York: Wiley.

    Google Scholar 

  • Cudeck, R. (1988), “Multiplicative models and MTMM matrices,” Journal of Educational Statistics, 13, 131–147.

    Article  Google Scholar 

  • Cudeck, R. (1989), “Analysis of correlation matrices using covariance structure models,” Psychological Bulletin, 105, 317–327.

    Article  Google Scholar 

  • Cudeck, R. (1991), “Noniterative factor analysis estimators with algorithms for subset and instrumental variable selection,” Journal of Educational Statistics, 16, 35–52.

    Article  Google Scholar 

  • Cudeck, R., and O’Dell, L. L. (1994), “Applications of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations,” Psychological Bulletin, 115, 475–487.

    Article  PubMed  Google Scholar 

  • Dijkstra, T. K. (1983), “Some comments on maximum likelihood and partial least squares methods,” Journal of Econometrics, 22, 67–90.

    Article  Google Scholar 

  • Dijkstra, T. K. (1992), “On statistical inference with parameter estimates on the boundary of the parameter space,” British Journal of Mathematical and Statistical Psychology, 45, 289–309.

    Article  Google Scholar 

  • Du Toit, S. H. C. (1979), The Analysis of Growth Curves, Ph. D. dissertation, Department of Statistics, University of South Africa.

    Google Scholar 

  • Fraser, C., and McDonald, R. P. (1988), “Covariance Structure Analysis, ” Multivariate Behavioral Research, 23, 263–265.

    Article  Google Scholar 

  • GAUSS, Version 3.1 (1993), Systems and Graphics Manual, Aptech Systems, Kent, Washington.

    Google Scholar 

  • Gill, P. E., Murray, W., and Wright, M. H. (1981), Practical Optimisation, London: Academic Press.

    Google Scholar 

  • Guttman, L. (1954), “A new approach to factor analysis: the radex,” in P F. Lazarsfeld (ed.), Mathematical Thinking in the Social Sciences, pp. 258–348, Glencoe, IL: The Free Press.

    Google Scholar 

  • Hartmann, W. M. (1992), The CALLS procedure: Extended User’s Guide, Cary, NC: The SAS Institute.

    Google Scholar 

  • Ihara, M., and Kano, Y. (1986), “A new estimator of the uniqueness in factor analysis,” Psychometrika, 51, 563–566.

    Article  Google Scholar 

  • Jennrich, R. I. (1970), “An asymptotic chi-square test for the equality of two correlation matrices,” Journal of the American Statistical Association, 65, 904–912.

    Google Scholar 

  • Jennrich, R. I., and Clarkson, D.B. (1980), “A feasible method for standard errors of estimate in maximum likelihood factor analysis,” Psychometrika, 45, 237–247.

    Article  Google Scholar 

  • Jennrich, R. I., and Robinson, S. M. (1969), “A Newton-Raphson algorithm for maximum likelihood factor analysis,” Psychometrika, 34, 111–123.

    Article  Google Scholar 

  • Jennrich, R. I., and Sampson, P. F. (1966), “Rotation for simple loadings,” Psychometrika, 31, 313–323.

    Article  PubMed  Google Scholar 

  • Jennrich, R. I., and Sampson, P. F. (1968), “Application of stepwise regression to nonlinear estimation,” Technometrics, 10, 63–72.

    Article  Google Scholar 

  • Jöreskog, K. G. (1967), “Some contributions to maximum likelihood factor analysis,” Psychometrika, 32, 443–482.

    Article  Google Scholar 

  • Jöreskog, K. G. (1969), “A general approach to confirmatory maximum likelihood factor analysis” Psychometrika 34, 183–202

    Google Scholar 

  • Jöreskog, K. G. (1970), “Estimation and testing of simplex models,” British Journal of Mathematical and Statistical Psychology, 23, 121–145.

    Article  Google Scholar 

  • Jöreskog, K. G. (1973), “A general method for estimating a linear structural equation system,” in: A. S. Goldberger and O. D. Duncan (eds.), Structural Equation Models in the Social Sciences, New York: Academic Press, pp. 85–112.

    Google Scholar 

  • Jöreskog, K. G., and Sörbom, D. (1986), LISREL VI: Analysis of Linear Structural Relationships by Maximum Likelihood, Instrumental Variables and Least Squares Methods, Users Guide, Mooresville, IN: Scientific Software.

    Google Scholar 

  • Jöreskog, K. G., and Sörbom, D.(1993), LISREL 8: Structural Equation Modeling with the SIMPLIS Command LanguageHillsdale, NJ: Erlbaum.

    Google Scholar 

  • Kaiser, H. F. (1958), “The Varimax Criterion for Oblique Rotation in Factor Analysis,” Psychometrika, 23, 187–200.

    Article  Google Scholar 

  • Kano, Y. (1986), “Conditions on Consistency of Estimators in Covariance Structure Model,” Journal of the Japanese Statistical Society, 13, 137–144.

    Google Scholar 

  • Kano, Y. (1990), “Non-iterative Estimation and the Choice of the Number of Factors in Exploratory Factor Analysis,” P sychometrika 55, 277–291.

    Google Scholar 

  • Keesling, J. W. (1972), “Maximum Likelihood Approaches to Causal Analysis,” unpublished Ph.D. dissertation. Department of Education, University of Chicago.

    Google Scholar 

  • Kiiveri, H. T. (1987), “An Incomplete Data Approach to the Analysis of Covariance Structures,” Psychometrika, 52, 539–554.

    Article  Google Scholar 

  • Küsters, U. (1987), Hierarchische Mittelwert-und Kovarianzstrukturmodelle mit nichtmetrischen endogenen Variablen. Heidelberg: Physica Verlag.

    Book  Google Scholar 

  • Küsters, U. (1990), “A note on sequential ML estimates and their asymptotic covariance matrices, Statistical Papers 31 131–145.

    Google Scholar 

  • Lawley, D. N. (1940), “The Estimation of Factor Loadings by the Method of Maximum Likelihood,” Proceedings of the Royal Society of Edinburgh, 60, 64–82.

    Google Scholar 

  • Lee, S., and Hershberger, S. (1990), “A simple rule for generating equivalent models in covariance structure modeling,” Multivariate Behavioral Research, 25, 313–334.

    Article  Google Scholar 

  • Lee, S. Y., and Jennrich, R. I. (1979), “A Study of Algorithms for Covariance Structure Analysis with Specific Comparisons Using Factor Analysis,” Psychometrika, 44, 99–113.

    Article  Google Scholar 

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

    Google Scholar 

  • MacCallum, R. C., Wegener, D. T., Uchino, B. N., and Fabrigar, L. R. (1993), “The problem of equivalent models in applications of covariance structure analysis,” Psychological Bulletin, 114, 185–199.

    Article  PubMed  Google Scholar 

  • Magnus, J. R., and Neudecker, H. (1988) Matrix Differential Calculus with Applications in Statistics and Econometrics. New York: Wiley.

    Google Scholar 

  • Mardia, K.V. (1970), “Measures of multivariate skewness and kurtosis with applications,” Biometrika, 57, 519–530.

    Article  Google Scholar 

  • McArdle, J. J. (1970), “The development of general multivariate software,” in: J. J. Hirschbuhl (ed.), Proceedings of the Association for the Development of Computer-based Instructional Systems, Akron, OH: University of Akron Press.

    Google Scholar 

  • McArdle, J. J., and McDonald, R. P. (1984), “Some algebraic properties of the Reticular Action Model for moment structures,” Journal of Mathematical and Statistical Psychology, 37, 234–251.

    Article  Google Scholar 

  • McDonald, R. P. (1975), Factor Analysis and Related Methods, Hillsdale, NJ: Erlbaum. McDonald, R. P. (1980), “A simple comprehensive model for the analysis of covariance structures: some remarks on applications,”British Journal of Mathematical and Statistical Psychology, 33, 161–183.

    Google Scholar 

  • McDonald, R. P., Parker, P. M., and Ishizuka, T. (1993), “A scale invariant treatment for recursive path models,” Psychometrika, 58, 431–443.

    Article  Google Scholar 

  • McKelvey, R. D., and Zavoina, W. (1975), “A Statistical Model for the Analysis of Ordinal Level Dependent Variables,” Journal of Mathematical Sociology 4, 103–120.

    Article  Google Scholar 

  • Miller, J. D., Hoffer, T., Suchner, R. W., Brown, K. G., and Nelson, C. (1992), LSAY Codebook: Student,Parent, andTeacher Datafor Cohort Two for Longitudinal Years One through Four (1987–1991), Vol. 2, Northern Illinois University, De Kalb, 60115–2854.

    Google Scholar 

  • Muthén, B. O. (1984), “A General Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Variable Indicators,” Psychometrika 49, 115–132.

    Article  Google Scholar 

  • Muthén, B. O. (1988), LISCOMP: Analysis of Linear Structural Equations with a Comprehensive Measurement Model, Mooresville, IN: Scientific Software.

    Google Scholar 

  • Muthén, B. O., and Arminger, G. (1994), “Bayesian Latent Variable Regression for Binary and Continuous Response Variables Using the Gibbs Sampler,” unpublished manuscript, Graduate School of Education, University of California, L.A.

    Google Scholar 

  • Muthén, B. O., and Kaplan, D. (1992), “A comparison of some methodologies for the factor analysis of non-normal Liken variables: A note on the size of the model,” British Journal of Mathematical and Statistical Psychology, 45, 19–30.

    Article  Google Scholar 

  • Muthén, B. O., and Satorra, A. (1994), “Technical Aspects of Muthén’s LISCOMP Approach to Estimation of Latent Variable Relations with a Comprehensive Measurement Model,” under review in Psychometrika.

    Google Scholar 

  • Neale, M. C. (1991), Mx: Statistical Modeling, Department of Human Genetics, Box 3 MCV, Richmond, VA 23298.

    Google Scholar 

  • Olsson, U. (1979), “Maximum Likelihood Estimation of the Polychoric Correlation Coefficient,” Psychometrika, 44, 443–460.

    Article  Google Scholar 

  • Olsson, U., Drasgow F., and Dorans, N. J. (1982), “The Polyserial Correlation Coefficient,” Psychometrika, 47, 337–347.

    Article  Google Scholar 

  • Pearson, K. (1900), “Mathematical Contributions to the Theory of Evolution in the Inheritance of Characters Not Capable of Exact Quantitative Measurement,” VIII. Philosophical Transactions of the Royal Society A, 195, 1075–1090.

    Google Scholar 

  • Rao, C. R. (1958), “Some statistical methods for comparison of growth curves,” Biometrika, 14, 1–17.

    Google Scholar 

  • Rosett, R. N., and Nelson, F. D. (1975), “Estimation of the two-limit probit regression model,” Econometrica, 43, 141–146.

    Article  Google Scholar 

  • Satorra, A. (1989), “Alternative test criteria in covariance structure analysis: a unified approach,” Psychometrika, 54, 131–151.

    Article  Google Scholar 

  • Satorra, A. (1993), “Asymptotic robust inferences in multi-sample analysis of augmentedmoment structures,” in C. M. Cuadras and C. R. Rao (eds.), Multivariate Analysis: Future Directions 2, 211–229, Amsterdam: North Holland.

    Google Scholar 

  • Satorra, A., and Saris, W. E. (1985), “The power of the likelihood ratio test in covariance structure analysis,” Psychometrika, 50, 83–90.

    Article  Google Scholar 

  • Shapiro, A. (1983), “Asymptotic distribution theory in the analysis of covariance structures,” South African Statistical Journal, 17, 33–81.

    Google Scholar 

  • Shapiro, A. (1984), “A Note on the Consistency of Estimators in the Analysis of Moment Structures,” British Journal of Mathematical and Statistical Psychology, 37, 84–88.

    Article  Google Scholar 

  • Shapiro, A. (1985a), “Asymptotic Equivalence of Minimum discrepancy Estimators to GLS Estimators,” South African Statistical Journal, 19, 73–81.

    Google Scholar 

  • Shapiro, A. (1985b), “Asymptotic distribution of test statistics in the analysis of moment structures under inequality constraints,” Biometrika, 72, 133–184.

    Article  Google Scholar 

  • Shapiro, A. (1987), “Robustness Properties of the MDF Analysis of Moment Structures,” South African Statistical Journal, 21, 39–62.

    Google Scholar 

  • Shapiro, A., and Browne, M. W. (1989), “On the Asymptotic Bias of Parameters under Parameter Drift,” Statistics and Probability Letters, 7, 221–224.

    Article  Google Scholar 

  • Shapiro, A., and Browne, M. W. (1990), “On the Treatment of Correlation Structures as Covariance Structures,” Linear Algebra and its Applications, 127, 567–587.

    Article  Google Scholar 

  • Schepers, A., and Arminger, G. (1992), MECOSA: A Program for the Analysis of General Mean-and Covariance Structures with Non-Metric Variables, User Guide,Frauenfeld: SLI-AG, Zürcher Str. 300, CH-8500 Frauenfeld, Switzerland.

    Google Scholar 

  • Schoenberg, R., and Arminger, G. (1988), LINCS2: A Program for Linear Covariance Structure Analysis, Kensington, MD: RJS Software.

    Google Scholar 

  • Sobel, M., and Arminger, G. (1992), “Modeling Household Fertility Decisions: A Nonlinear Simultaneous Probit Model,” Journal of the American Statistical Association, 87, 38–47.

    Article  PubMed  Google Scholar 

  • Steiger, J. H. (1990), “Structural model evaluation and modification: an interval estimation approach,” Multivariate Behavioral Research, 25, 173–180.

    Article  Google Scholar 

  • Steiger, J. H. (1994), Structural Equation Modeling: Technical Documentation,StatSoft: STATISTICA (in press).

    Google Scholar 

  • Steiger, J. H., and Lind, J. C. (1980), “Statistically based tests for the number of common factors,” paper presented at the annual meeting of the Psychometric Society, Iowa City, Iowa.

    Google Scholar 

  • Steiger, J. H., Shapiro, A., and Browne, M. W. (1985), “On the multivariate asymptotic distribution of sequential chi-square test statistics,” Psychometrika, 50, 253–264.

    Article  Google Scholar 

  • Stewart, M. B. (1983), “On Least Squares Estimation When the Dependent Variable Is Grouped,” Review of Economic Studies, 50, 737–753.

    Article  Google Scholar 

  • Swain, A. J. (1975a), “A Class of Factor Analysis Estimation Procedures with Common Asymptotic Properties,” Psychometrika, 40, 315–335.

    Article  Google Scholar 

  • Swain, A. J. (1975b), Analysis of Parametric Structures for Variance Matrices,unpublished Ph.D. dissertation, University of Adelaide.

    Google Scholar 

  • Tobin, J. (1958), “Estimation of Relationships for Limited Dependent Variables,” Econometrica, 26, 24–36.

    Article  Google Scholar 

  • Tucker, L. R. (1958), “Determination of parameters of a functional relation by factor analysis,” Psychometrika, 23, 19–23.

    Article  Google Scholar 

  • Wald, H. (1943), “Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations Is Large,” Transactions of the American Mathematical Society, 54, 426–482.

    Article  Google Scholar 

  • Wiley, D. E. (1973), “The Identification Problem for Structural Equation Models with Unmeasured Variables,” in A. S. Goldberger and O. D. Duncan (eds.), Structural Equation Models in the Social Sciences, New York: Academic Press, 69–83.

    Google Scholar 

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Browne, M.W., Arminger, G. (1995). Specification and Estimation of Mean- and Covariance-Structure Models. In: Arminger, G., Clogg, C.C., Sobel, M.E. (eds) Handbook of Statistical Modeling for the Social and Behavioral Sciences. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1292-3_4

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