Skip to main content
Log in

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

  • Published:
Psychometrika Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • 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 

  • Bentler, P. M., & Tanaka, J. S. (1983). Problems with EM algorithms for ML factor analysis.Psychometrika, 48, 247–251.

    Google Scholar 

  • 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 

  • 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 Unpublished doctoral dissertation, University of Groningen, Groningen.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 

  • 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 

  • Hägglund, G. (1982). Factor analysis by instrumental variable methods.Psychometrika, 47, 209–222.

    Google Scholar 

  • IMSL (1982).IMSL Library. Reference Manual. (Vol. 2, 9th ed.). Houston, TX: International Mathematical and Statistical Libraries.

    Google Scholar 

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

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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.

  • Lee, S. Y. (1980). Estimation of covariance structure models with parameters subject to functional restraints.Psychometrika, 45, 309–324.

    Google Scholar 

  • 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 

  • Rindskopf, D. (1983). Parameterizing inequality constraints on unique variances in linear structural models.Psychometrika, 48, 73–83.

    Google Scholar 

  • Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models.Psychometrika, 49, 37–47.

    Google Scholar 

  • Rubin, D. B., & Thayer, D. T. (1982). EM algorithms for ML factor analysis.Psychometrika, 47, 69–76.

    Google Scholar 

  • Rubin, D. B., & Thayer, D. T. (1983). More on EM for ML factor analysis.Psychometrika, 48, 253–257.

    Google Scholar 

  • Tamura, Y., & Fukutomi, K. (1970). On the improper solutions in factor analysis.TRU Mathematics, 6, 63–71.

    Google Scholar 

  • Van Driel, O. P. (1978). On various causes of improper solutions in maximum likelihood factor analysis.Psychometrika, 43, 225–243.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Boomsma, A. Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation. Psychometrika 50, 229–242 (1985). https://doi.org/10.1007/BF02294248

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02294248

Key words

Navigation