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Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index
Comparative fit indexes in structural models.
  • P. Bentler
  • Mathematics
    Psychological bulletin
  • 27 February 1990
A new coefficient is proposed to summarize the relative reduction in the noncentrality parameters of two nested models and two estimators of the coefficient yield new normed (CFI) and nonnormed (FI) fit indexes.
Significance Tests and Goodness of Fit in the Analysis of Covariance Structures
Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for
Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification
This study evaluated the sensitivity of maximum likelihood (ML)-, generalized least squares (GLS)-, and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under
Comparative Fit Indices in Structural Models
otJ.x?tNrtr;A;rtt7e- eta, Comparative Fit lndexes in Structural Models P. M. Bentler University of California, Los Angeles Norincd and nonnormed fit indexes are frequently used as adjuncts to
On the fit of models to covariances and methodology to the Bulletin.
  • P. Bentler
  • Psychology
    Psychological bulletin
  • 1 November 1992
It is noted that 7 of the 10 top-cited articles in the Psychological Bulletin deal with methodological topics. One of these is the Bentler-Bonett (1980) article on the assessment of fit in covariance
A scaled difference chi-square test statistic for moment structure analysis
Research supported by the Spanish DGES grant PB96-0300, and USPHS grants DA00017 and DA01070.
Practical Issues in Structural Modeling
Practical problems that are frequently encountered in applications of covariance structure analysis are discussed and solutions are suggested. Conceptual, statistical, and practical requirements for