A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data

By C. Distefano, Grant B. Morgan. 2014

627 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e077d0451bad9c3788ad503485b779973d67bb91

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit (2017, neutral): “As with in previous studies (e.g., DiStefano & Morgan, 2014; Muthén & Kaplan, 1985), item-level nonnormal data were generated by discretizing continuous, normal data through the application of a set of threshold values.”
Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit (2018, methodology): “The item-level nonnormal data with desired skewness and kurtosis were generated by discretizing continuous data through selected thresholds (DiStefano & Morgan, 2014; Maydeu-Olivares, 2017b; Muthén & Kaplan, 1985).”
Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA (2020, methodology): “To take into account the ordinal nature of the data, under the SEM framework, a better approach is to fit an ordinal factor analysis model, especially if the data have fewer than five ordered categories (DiStefano & Morgan, 2014; Muthén & Kaplan, 1992; Rhemtulla, Brosseau-Liard, & Savalei, 2012).”
Should Regularization Replace Simple Structure Rotation in Exploratory Factor Analysis? (2019, background): “Especially the use of Likert scales may have profound consequences on the estimation performance (DiStefano, 2002; DiStefano & Morgan, 2014).”
Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data (2018, methodology): “…under WLS (Bandalos, 2014; DiStefano & Morgan, 2014; Flora & Curran, 2004); however, if sample sizes were small and data moderately or extremely asymmetric, then problems, such as convergence or inaccurate parameter estimates, could be observed (Bandalos, 2014; DiStefano & Morgan, 2014; Lei, 2009).”
RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods (2018, background): “DiStefano and Morgan (2014) found that RMSEAS and CFIS using DWLS produce problematic results under highly asymmetric categorical distributions, small sample sizes, and dichotomous data.”
Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness? (2018, methodology): “These levels were used in similar studies involving categorical data (e.g., Bandalos, 2008; DiStefano & Morgan, 2014; Rhemtulla, Brosseau-Liard, & Savalei, 2012).”