A Statistically Justified Pairwise ML Method for Incomplete Nonnormal Data: A Comparison With Direct ML and Pairwise ADF

By Victoria Savalei, Peter M. Bentler. 2005

92 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7316dce03fb594fbe55f151b93648b75d3bb5332

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In Statistics and Probability

Citations in the corpus

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).”
Is the ML Chi-Square Ever Robust to Nonnormality? A Cautionary Note With Missing Data (2008, background): “These have been found to perform remarkably well (Enders, 2001a; Gold, Bentler, & Kim, 2003; Savalei & Bentler, 2005).”
A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables (2009, methodology): “Finally, following the advice of Savalei and Bentler (2005) , care was taken to generate data in such a way as to create about the same number of missing data patterns in MCAR and MAR data sets.”
Small Sample Statistics for Incomplete Nonnormal Data: Extensions of Complete Data Formulae and a Monte Carlo Comparison (2010, background): “As long as ML estimates are consistent, test statistics can be expected to perform similarly regardless of type of missingness (e.g., Gold et al., 2003), and their performance depends more on the number of missing data patterns (Savalei & Bentler, 2005).”
Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data (2009, methodology): “In addition, although this technique was developed under MCAR, missingness in ADF can hardly yield improved performance (Gold et al., 2003; Savalei & Bentler, 2005; van Praag et al., 1985).”
Comparison of Methods of Handling Missing Time-Invariant Covariates in Latent Growth Models Under the Assumption of Missing Completely at Random (2007, methodology): “Two types of EM algorithms are implemented in SEM: the unstructured EM algorithm and the structured EM algorithm (see Savalei & Bentler, 2005).”
Understanding Robust Corrections in Structural Equation Modeling (2014, background): “Many other types of similar corrections are possible, some of which are illustrated in this article (Savalei & Bentler, 2005, 2009; Yuan & Bentler, 2000; Yuan Correspondence should be addressed to Victoria Savalei, Department of Psychology, University of British Columbia, 3410-2136 West Mall,…”
Subjective evaluations of intelligence and academic self-concept predict academic achievement: Evidence from a selective student population (2009, unknown): “The pairwise ML method (Savalei & Bentler, 2005) provides computed statistics for correlations based on all available cases that have scores on pairs of variables.”