Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation

By D. Bandalos. 2014

368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fcd553e30aac044d51e1eec3b336fb2ede9c0947

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Citations in the corpus

The Development and Validation of the State Self-Compassion Scale (Long- and Short Form) (2020, methodology): “…squares mean- and variance-adjusted estimator (WLSMV) which has been shown to be superior for ordinal indicators (such as Likert ratings) compared with maximum-likelihoodbased estimators particularly in the case of five or fewer response options (e.g., Bandalos 2014; Finney and DiStefano 2013).”
Conceptualizing and testing a new tripartite measure of coach interpersonal behaviors (2019, methodology): “Subsequent analyses were conducted using a robust maximum likelihood estimator (MLR) which provides robust fit indices and standard errors in the case of non-normality and performs well with variables with a minimum of five response categories (Bandalos, 2014; Rhemtulla, Brosseau-Laird, & Savalei, 2”
Comparison of Different Estimation Methods for Categorical and Ordinal Data in Confirmatory Factor Analysis (2015, background): “In social sciences, data are often collected by scoring multiple-choice items in two categories and ordinal items with three or more categories, but not by an equal-interval scale (Bandalos, 2014).”
Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General (2018, methodology): “MLR for binary and ordinal variables ≥200 - 500 Bandalos (2014)”
Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data (2018, methodology): “Other researchers did not observe substantial differences between different estimators (e.g., Bandalos, 2014; Flora & Curran, 2004; Savalei & Rhemtulla, 2013; Yang-Wallentin et al., 2010); however, this may be due to the smaller sizes of the tested models in these studies, where typically less than…”
Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness? (2018, result): “Such corrections have been found to produce χ2 fit indexes close to expected values, even when model size increased or sample size decreased (Bandalos, 2014; DiStefano & Morgan, 2014; Flora & Curran, 2004; Yang-Wallentin, Jöreskog, & Luo, 2010).”
Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA (2020, background): “Consequently, ULS and DWLS have emerged in the literature as recommended options (Bandalos, 2014; Forero, Maydeu-Olivares, & Gallardo-Pujol, 2009; Savalei & Rhemtulla, 2013).”