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A Comment on a Paper by H. Wu and M. W. Browne (2014)

Published online by Cambridge University Press:  01 January 2025

Albert Satorra
Albert Satorra*
Affiliation:
Universitat Pompeu Fabra
*
Correspondence should be made to Albert Satorra, Universitat Pompeu Fabra, Barcelona, Spain. Email: albert.satorra@upf.edu
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Abstract

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Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 3 , September 2015 , pp. 613 - 618
Copyright
Copyright © 2014 The Psychometric Society

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Footnotes

Work supported by grant EC02011-28875 from the Spanish Ministry of Science and Innovation.

References

Browne, M.W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230–258.CrossRefGoogle Scholar
Chen, C.-F., (1979). Bayesian inference for a normal dispersion matrix and its application to stochastic multiple regression analysis. Journal of the Royal Statistical Society, Series B, 41, 235–248.CrossRefGoogle Scholar
Chun, S. Y., & Shapiro, A., (2009). Normal versus noncentral chi-square asymptotic of misspecified models. Multivariate Behavioral Research, 44, 803–827.CrossRefGoogle Scholar
R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.Google Scholar
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.CrossRefGoogle Scholar
Satorra, A. (1989). Alternative test criteria in covariance structure analysis: A unified approach. Psychometrika, 54(1), 131–151.CrossRefGoogle Scholar
Satorra, A., & Bentler, P.M. (1994). Correctios to test statistics and standard errors in covariance structure analysis. In van Eye, A., & Clogg, C.C. (Eds.), Latent variable analysis in developmental research (pp. 285–305). Thousand Oaks, CA: Sage Publications.Google Scholar
Satorra, A., & Saris, W.E. (1983). The accuracy of a procedure for calculating the power of the likelihood ratio test as used within the LISREL framework. In Middendorp, C.P., Niemoller, B., & Saris, W.E. (Eds.), Sociometric Research 1982 (pp. 127–190). Amsterdam: Sociometric Research Foundation.Google Scholar
Satorra, A., & Saris, W.E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50(1), 83–89.CrossRefGoogle Scholar
Satorra, A., Saris, W.E., & de Pijper, W.M. (1991). A comparison of several approximations to the power function of the likelihod ratio test in covariance structure analysis. Statistica Neerlandica, 45, 173–185.CrossRefGoogle Scholar
Wald, A. (1943). Test of statistical hypothesis concerning several parameters when the number of observations is large. Transactions of the American Mathematical Society, 54, 426–482.CrossRefGoogle Scholar