Is the ML Chi-Square Ever Robust to Nonnormality? A Cautionary Note With Missing Data

By Victoria Savalei. 2008

71 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Ecology, Evolution, Behavior and Systematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/61dd1cb543adb3f7f7675df39b4509f9c08341ce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Victoria Savalei
2. H. Freudenthaler
3. M. Bühner
4. Moritz Heene

In Statistics and Probability

1. M. Bühner
2. Victoria Savalei
3. Carl F. Falk
4. Paul T. von Hippel
5. J. Veale
6. Dexin Shi
7. C. Distefano
8. Kristopher J Preacher

Citations in the corpus

• Sensitivity of SEM Fit Indexes With Respect to Violations of Uncorrelated Errors (2012, background): “It is known that deviations from multivariate normality can affect the performance of the chi-square model test (Bollen & Stine, 1992; Lei & Lomax, 2005; Savalei, 2008), but data in the social sciences often fail to fulfill this requirement.”
• Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison (2010, methodology): “All tables include the usual ML chi-square for comparison as well as verification that nonnormal data were not created in a way as to make it robust to nonnormality (e.g., Satorra & Bentler, 1990; Savalei, 2008).”