A Comparison of Maximum-Likelihood and Asymptotically Distribution-Free Methods of Treating Incomplete Nonnormal Data

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Is the ML Chi-Square Ever Robust to Nonnormality? A Cautionary Note With Missing Data (2008, unknown): “These have been found to perform remarkably well ( Enders, 2001a ; Gold, Bentler, & Kim, 2003 ; Savalei & Bentler, 2005 ).”
A Statistically Justified Pairwise ML Method for Incomplete Nonnormal Data: A Comparison With Direct ML and Pairwise ADF (2005, methodology): “The design of the study is similar to that of Gold et al. (2003), with the following modifications: Intermediate sample sizes are added, observed and not expected information matrix is used in the computations (standard errors based on observed information might work better in smaller sample sizes;…”
Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data (2009, methodology): “Gold, Bentler, and Kim (2003) reported in their missing data study that robust ML produced less biased parameter estimates than did ADF.”
Robust Two-Stage Approach Outperforms Robust Full Information Maximum Likelihood With Incomplete Nonnormal Data (2014, methodology): “The goal of this study was to compare the relatively new robust TS approach for estimating and fitting structural equation models with incomplete nonnormal data to the best currently available analytic method for such data, robust FIML (Enders, 2001; Gold et al., 2003; Savalei & Bentler, 2005).”