Hostname: page-component-76c49bb84f-lxspr Total loading time: 0 Render date: 2025-07-09T07:39:07.400Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

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