Hostname: page-component-5f745c7db-nzk4m Total loading time: 0 Render date: 2025-01-06T22:37:52.978Z Has data issue: true hasContentIssue false

Improper Solutions in the Analysis of Covariance Structures: Their Interpretability and a Comparison of Alternate Respecifications

Published online by Cambridge University Press:  01 January 2025

David W. Gerbing*
Affiliation:
Baylor University
James C. Anderson
Affiliation:
J. L. Kellogg Graduate School of Management, Northwestern University
*
Requests for reprints should be sent to David W. Gerbing, Department of Psychology, Baylor University, Waco, TX 76798.

Abstract

A Monte Carlo approach was employed to investigate the interpretability of improper solutions caused by sampling error in maximum likelihood confirmatory factor analysis. Four models were studied with two sample sizes. Of the overall goodness-of-fit indices provided by the LISREL VI program significant differences between improper and proper solutions were found only for the root mean square residual. As expected, indicators of the factor on which the negative uniqueness estimate occurred had biased loadings, and the correlations of its factor with other factors were also biased. In contrast, the loadings of indicators on other factors and those factor intercorrelations did not have any bias of practical significance. For initial solutions with one negative uniqueness estimate, three respecifications were studied: Fix the uniqueness at .00, fix it at .20, or constrain the domain of the solution to be proper. For alternate, respecified solutions that were converged and proper, the constrained solutions and uniqueness fixed at .00 solutions were equivalent. The mean goodness-of-fit and pattern coefficient values for the original improper solutions were not meaningfully different from those obtained under the constrained and uniqueness fixed at .00 respecifications.

Type
Original Paper
Copyright
Copyright © 1987 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This investigation was supported in part by a grant from the Baylor University Research Committee (#018-F83-URC). The authors gratefully acknowledge the comments and suggestions of Claes Fornell and Roger E. Kirk, and the assistance of Timothy J. Vance with the analysis.

References

Anderson, James C., Gerbing, David W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155173.CrossRefGoogle Scholar
Andrews, Frank M. (1984). Construct validity and error components of survey measures: A structural modeling approach. Public Opinion Quarterly, 48, 409442.CrossRefGoogle Scholar
Bentler, Peter M. (1976). Multistructural statistical models applied to factor analysis. Multivariate Behavioral Research, 11, 325.CrossRefGoogle Scholar
Bentler, Peter M. (1983). Simultaneous equation systems as moment structure models. Journal of Econometrics, 22, 1342.CrossRefGoogle Scholar
Bentler, Peter M. (1984). Theory and implementation of EQS: A structural equations program, Los Angeles: BMDP Statistical Software.Google Scholar
Bentler, Peter M., Lee, Sik-Yum (1979). Newton-Raphson approach to exploratory and confirmatory maximum likelihood factor analysis. Journal of the Chinese University of Hong Kong, 5, 562573.Google 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 (Part I) (pp. 149173). 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
Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika, 50, 229242.CrossRefGoogle Scholar
Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 6283.CrossRefGoogle ScholarPubMed
Fornell, C. (1983). Issues in the application of covariance structure analysis: A comment. Journal of Consumer Research, 9, 443447.CrossRefGoogle Scholar
Gerbing, David W., Anderson, James C. (1985). The effects of sampling error and model characteristics on parameter estimates for maximum likelihood factor analysis. Multivariate Behavioral Research, 20, 255271.CrossRefGoogle Scholar
IMSL (1980). International mathematical and statistical libraries, Houston: Author.Google Scholar
Jackson, Douglas N., Chan, David W. (1980). Maximum-likelihood estimation in common factor analysis: A cautionary note. Psychological Bulletin, 88, 502508.CrossRefGoogle Scholar
Jöreskog, Karl G. (1967). Some contributions to maximum likelihood factor analysis. Psychometrika, 32, 443482.CrossRefGoogle Scholar
Jöreskog, Karl G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443477.CrossRefGoogle Scholar
Jöreskog, Karl G. (1979). Author's addendum to: A general approach to confirmatory maximum likelihood factor analysis. In Jöreskog, Karl G., Sörbom, D. (Eds.), Advances in factor analysis and structural equation models (pp. 4043). Cambridge, MA: Abt Books.Google Scholar
Jöreskog, Karl G., Sörbom, Dag (1984). LISREL VI: Analysis of linear structural relationships by the method of maximum likelihood, Mooresville, IN: Scientific Software.Google Scholar
Kirk, Roger E. (1982). Experimental design 2nd ed.,, Belmont, CA: Brooks/Cole.Google Scholar
Lawley, D. N., Maxwell, A. E. (1971). Factor analysis as a statistical method, London: Butterworths.Google Scholar
Lee, Sik-Yum (1980). Estimation of covariance structure models with parameters subject to functional restraints. Psychometrika, 45, 309324.CrossRefGoogle Scholar
Long, J. Scott (1983). Confirmatory factor analysis, Beverly Hills and London: Sage.CrossRefGoogle Scholar
McDonald, Roderick P. (1978). A simple comprehensive model for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 31, 5972.CrossRefGoogle Scholar
McDonald, Roderick P. (1979). The simultaneous estimation of factor loadings and scores. British Journal of Mathematics and Statistical Psychology, 32, 212228.CrossRefGoogle Scholar
Rindskopf, David (1983). Parameterizing inequality constraints on unique variances in linear structural models. Psychometrika, 48, 7383.CrossRefGoogle Scholar
Rindskopf, David (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49, 3747.CrossRefGoogle Scholar
Sörbom, Dag, Jöreskog, Karl G. (1982). A second generation of multivariate analysis. In Fornell, Claes (Eds.), Measurement and evaluation (pp. 381399). NY: Praeger.Google Scholar
van Driel, Otto P. (1978). On various causes of improper solutions in maximum likelihood factor analysis. Psychometrika, 43, 225243.CrossRefGoogle Scholar