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Nonconvergence, Improper Solutions, and Starting Values in Lisrel Maximum Likelihood Estimation

Published online by Cambridge University Press:  01 January 2025

Anne Boomsma
Anne Boomsma*
Affiliation:
University of Groningen
*
Requests for reprints should be sent to A. Boomsma, Vakgroep Statistiek en Meettheorie, Rijksuniversiteit Groningen, Oude Boteringestraat 23, 9712 GC Groningen, THE NETHERLANDS.
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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.

Keywords

maximum likelihood estimationLISRELDavidon-Fletcher-Powellstarting valuesnonconvergenceimproper solutionsrobustnessMonte Carlosmall sample results
Type
Original Paper
Information
Psychometrika , Volume 50 , Issue 2 , June 1985 , pp. 229 - 242
Copyright
Copyright © 1985 The Psychometric Society

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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.CrossRefGoogle Scholar
Bentler, P. M., & Tanaka, J. S. (1983). Problems with EM algorithms for ML factor analysis. Psychometrika, 48, 247–251.CrossRefGoogle Scholar
Boomsma, A. (1982). The robustness of LISREL against small sample sizes in factor analysis models. In Jöreskog, K. G. & Wold, H. (Eds.), Systems under indirect observation: causality, structure, prediction (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, Groningen: University of 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.CrossRefGoogle Scholar
IMSL (1982). IMSL Library. Reference Manual, Houston, TX: International Mathematical and Statistical Libraries.Google Scholar
Jöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis. Psychometrika, 32, 443–482.CrossRefGoogle Scholar
Jöreskog, K. G. (1977). Structural equation models in the social sciences: Specification, estimation, testing. In Krishnaiah, P.R. (Eds.), 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, 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 Cuttance, P. F. & Ecob, J. R. (Eds.), Structural modeling. Cambridge: Cambridge University Press.Google Scholar
Lee, S. Y. (1980). Estimation of covariance structure models with parameters subject to functional restraints. Psychometrika, 45, 309–324.CrossRefGoogle 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.CrossRefGoogle Scholar
Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49, 37–47.CrossRefGoogle Scholar
Rubin, D. B., Thayer, D. T. (1982). EM algorithms for ML factor analysis. Psychometrika, 47, 69–76.CrossRefGoogle Scholar
Rubin, D. B., Thayer, D. T. (1983). More on EM for ML factor analysis. Psychometrika, 48, 253–257.CrossRefGoogle 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.CrossRefGoogle Scholar