Abstract
A Monte Carlo study assessed the effect of sampling error and model characteristics on the occurrence of nonconvergent solutions, improper solutions and the distribution of goodness-of-fit indices in maximum likelihood confirmatory factor analysis. Nonconvergent and improper solutions occurred more frequently for smaller sample sizes and for models with fewer indicators of each factor. Effects of practical significance due to sample size, the number of indicators per factor and the number of factors were found for GFI, AGFI, and RMR, whereas no practical effects were found for the probability values associated with the chi-square likelihood ratio test.
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James Anderson is now at the J. L. Kellogg Graduate School of Management, Northwestern University. The authors gratefully acknowledge the comments and suggestions of Kenneth Land and the reviewers, and the assistance of A. Narayanan with the analysis. Support for this research was provided by the Graduate School of Business and the University Research Institute of the University of Texas at Austin.
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Anderson, J.C., Gerbing, D.W. The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika 49, 155–173 (1984). https://doi.org/10.1007/BF02294170
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DOI: https://doi.org/10.1007/BF02294170