The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation
Abstract
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate.
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“DWLS does not assume multivariate normality of indicators and provides more accurate parameter estimates for ordinal data compared with maximum likelihood-based estimators.”
From Enhancing Digital Financial Literacy and Digital Trust for Sustainable Online Loan Adoption among Micro-Entrepreneurs in Developing Economies (2026) · cites this paper as [51]
In the citing paper
“The Diagonally Weighted Least Squares (DWLS) estimator with robust standard errors and a scaled/shifted test statistic was selected, as recommended for ordered categorical (Likert-scale) data [50], [51].”
From Enhancing Digital Financial Literacy and Digital Trust for Sustainable Online Loan Adoption among Micro-Entrepreneurs in Developing Economies (2026) · cites this paper as [51]
In the citing paper
“A sample size of > 200 seems sufficient for robust parameter estimations with ordinal data according to Bandalos and Forero and colleagues [44, 45].”
From Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis (2020) · cites this paper as [44]
In the citing paper
“In which sample sizes between 200 and 500 are recommended.”
From Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis (2020) · cites this paper as [44]
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MethodInfluentialThe Effect of Estimation Methods on SEM Fit Indices
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Metadata
- Authors
- Bandalos, D. L.
- Affiliations
- James Madison University (US)
- Corresponding author
- Bandalos, Deborah L.
- ORCID
- Bandalos, Deborah L.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2014-01-02
- Volume
- 21
- Issue
- 1
- Pages
- 102-116
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2085399079
- Cited by
- 381
- References
- 25
- Citations by year
- 2026: 43 · 2025: 54 · 2024: 53 · 2023: 53 · 2022: 44 · 2021: 33 · 2020: 40 · 2019: 20 · 2018: 19 · 2017: 13 · 2016: 5 · 2015: 3 · 2014: 1
- FWCI
- 9.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- categorical data, non-normal data, standard error bias, statistical power, type I error control, model misspecification, data asymmetry, sample size effects, weighted least squares
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