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Psychometrika

, Volume 50, Issue 2, pp 229–242 | Cite as

Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation

  • Anne Boomsma
Article

Abstract

In the framework of a robustness study on maximum likelihood estimation with LISREL three types of problems are dealt with: nonconvergence, improper solutions, and choice of starting values. The purpose of the paper is to illustrate why and to what extent these problems are of importance for users of LISREL. The ways in which these issues may affect the design and conclusions of robustness research is also discussed.

Key words

maximum likelihood estimation LISREL Davidon-Fletcher-Powell starting values nonconvergence improper solutions robustness Monte Carlo small sample results 

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References

  1. Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis.Psychometrika, 49, 155–173.Google Scholar
  2. Bentler, P. M., & Tanaka, J. S. (1983). Problems with EM algorithms for ML factor analysis.Psychometrika, 48, 247–251.Google Scholar
  3. Boomsma, A. (1982). The robustness of LISREL against small sample sizes in factor analysis models. In K. G. Jöreskog & H. Wold (Eds.),Systems under indirect observation: causality, structure, prediction (Part 1, pp. 149–173). Amsterdam: North-Holland.Google Scholar
  4. Boomsma, A. (1983).On the robustness of LISREL (maximum likelihood estimation) against small sample size and non-normality. Unpublished doctoral dissertation, University of Groningen, Groningen.Google Scholar
  5. Gruvaeus, G. T., & Jöreskog, K. G. (1970).A computer program for minimizing a function of several variables (Research Bulletin 70-14). Princeton, NJ: Educational Testing Service.Google Scholar
  6. Hägglund, G. (1982). Factor analysis by instrumental variable methods.Psychometrika, 47, 209–222.Google Scholar
  7. IMSL (1982).IMSL Library. Reference Manual. (Vol. 2, 9th ed.). Houston, TX: International Mathematical and Statistical Libraries.Google Scholar
  8. Jöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis.Psychometrika, 32, 443–482.Google Scholar
  9. Jöreskog, K. G. (1977). Structural equation models in the social sciences: Specification, estimation, testing. In P.R. Krishnaiah (Ed.),Applications of statistics (pp. 265–287). Amsterdam: North-Holland.Google Scholar
  10. Jöreskog, K. G., & Sörbom, D. (1981).LISREL V. Analysis of linear structural relationships by maximum likelihood and least squares methods (Research Report 81-8). Uppsala: University of Uppsala, Department of Statistics.Google Scholar
  11. Jöreskog, K. G., & Sörbom, D. (1984).LISREL VI. Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. User's guide. Uppsala: University of Uppsala, Department of Statistics.Google Scholar
  12. Kelderman, H. (in press). LISREL models for inequality constraints in factor and regression analysis. In P. F. Cuttance & J. R. Ecob (Eds.),Structural modeling. Cambridge: Cambridge University Press.Google Scholar
  13. Lee, S. Y. (1980). Estimation of covariance structure models with parameters subject to functional restraints.Psychometrika, 45, 309–324.Google Scholar
  14. Mattson, A., Olsson, U., & Rosèn, M. (1966).The maximum likelihood method in factor analysis with special consideration to the problem of improper solutions (Research Report). Uppsala: University of Uppsala, Department of Statistics.Google Scholar
  15. Rindskopf, D. (1983). Parameterizing inequality constraints on unique variances in linear structural models.Psychometrika, 48, 73–83.Google Scholar
  16. Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models.Psychometrika, 49, 37–47.Google Scholar
  17. Rubin, D. B., & Thayer, D. T. (1982). EM algorithms for ML factor analysis.Psychometrika, 47, 69–76.Google Scholar
  18. Rubin, D. B., & Thayer, D. T. (1983). More on EM for ML factor analysis.Psychometrika, 48, 253–257.Google Scholar
  19. Tamura, Y., & Fukutomi, K. (1970). On the improper solutions in factor analysis.TRU Mathematics, 6, 63–71.Google Scholar
  20. Van Driel, O. P. (1978). On various causes of improper solutions in maximum likelihood factor analysis.Psychometrika, 43, 225–243.Google Scholar

Copyright information

© The Psychometric Society 1985

Authors and Affiliations

  • Anne Boomsma
    • 1
  1. 1.Vakgroep Statistiek en Meettheorie, Rijksuniversiteit GroningenGroningenThe Netherlands

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