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- W2024598906 abstract "Robust corrections to standard errors and test statistics have wide applications in structural equation modeling (SEM). The original SEM development, due to Satorra and Bentler (1988 Satorra, A. and Bentler, P. M. 1988. “Scaling corrections for chi-square statistics in covariance structure analysis”. In ASA 1988 Proceedings of the Business and Economic Statistics Section, 308–313. Alexandria, VA: American Statistical Association. [Google Scholar], 1994 Satorra, A. and Bentler, P. M. 1994. “Corrections to test statistics and standard errors in covariance structure analysis”. In Latent variables analysis: Applications for developmental research, Edited by: von Eye, A. and Clogg, C. C. 399–419. Thousand Oaks, CA: Sage. [Google Scholar]), was to account for the effect of nonnormality. Muthén (1993) Muthén, B. O. 1993. “Goodness of fit with categorical and other nonnormal variables”. In Testing structural equation models, Edited by: Bollen, K. A. and Long, J. S. 205–234. Newbury Park, CA: Sage. [Google Scholar] proposed corrections to accompany certain categorical data estimators, such as cat-LS or cat-DWLS. Other applications of robust corrections exist. Despite the diversity of applications, all robust corrections are constructed using the same underlying rationale: They correct for inefficiency of the chosen estimator. The goal of this article is to make the formulas behind all types of robust corrections more intuitive. This is accomplished by building an analogy with similar equations in linear regression and then by reformulating the SEM model as a nonlinear regression model." @default.
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- W2024598906 date "2014-01-02" @default.
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- W2024598906 title "Understanding Robust Corrections in Structural Equation Modeling" @default.
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- W2024598906 doi "https://doi.org/10.1080/10705511.2013.824793" @default.
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