The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis

James C. Anderson*, David W. Gerbing

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

793 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)155-173
Number of pages19
JournalPsychometrika
Volume49
Issue number2
DOIs
StatePublished - Jun 1984

Keywords

  • Confirmatory factory analysis
  • LISREL
  • Maximum likelihood
  • Monte Carlo

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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