The Model-Size Effect on Traditional and Modified Tests of Covariance Structures

  title={The Model-Size Effect on Traditional and Modified Tests of Covariance Structures},
  author={Walter Herzog and Anne Boomsma and Sven Reinecke},
According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that the traditional maximum likelihood ratio statistic, TML, overestimates nominal Type I error rates up to 70% under conditions of multivariate normality… CONTINUE READING
15 Extracted Citations
66 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 15 extracted citations

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 66 references

Mplus: Statistical analysis with latent variables: Technical appendices. Los Angeles: Muthén & Muthén

  • B. O. Muthén
  • 2004
Highly Influential
4 Excerpts

Tests of significance in factor analysis

  • M. S. Bartlett
  • British Journal of Psychology ( Statistical…
  • 1950
Highly Influential
13 Excerpts

Effect of the number of variables on measures of fit in structural equation modeling

  • D. A. Kenny, D. B. McCoach
  • Structural Equation Modeling,
  • 2003
Highly Influential
5 Excerpts

Performance of modified test statistics in covariance and correlation structure analysis under conditions of multivariate nonnormality

  • R. T. Fouladi
  • Structural Equation Modeling,
  • 2000
Highly Influential
11 Excerpts

The robustness of estimation methods for covariance structure analysis

  • J. J. Hoogland
  • Unpublished doctoral dissertation,
  • 1999
Highly Influential
6 Excerpts

An introduction to multivariate statistical analysis

  • T W.
  • binomial proportions. The American Statistician,
  • 1958
Highly Influential
7 Excerpts

Approximate is better than “ exact ” for interval estimation of binomial proportions

  • A. Agresti, B. A. Coull
  • The American Statistician
  • 1998
Highly Influential
1 Excerpt

Approximate is better than “exact” for interval estimation

  • A. Agresti, B. A. Coull
  • 1998
Highly Influential
2 Excerpts

Similar Papers

Loading similar papers…