The Effect of Sampling Error on Convergence, Improper Solutions, and Goodness-of-Fit Indices for Maximum Likelihood Confirmatory Factor Analysis
1984/06/01 by James C. Anderson, David W. Gerbing · 1,829 citations
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Computer science #Confirmatory factor analysis #Econometrics #Goodness of fit #Likelihood-ratio test #Mathematics #Maximum likelihood #Monte Carlo method #Psychometric Methodologies and Testing #Reliability and Agreement in Measurement #Sample (material) #Sample size determination #Sampling (signal processing) #Statistics #Structural equation modeling
paper · doi:10.1007/bf02294170
published in Psychometrika 49(2), 155-173 (Springer Science+Business Media)
openalex publication_date 1984/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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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