Relative Performance of Categorical Diagonally Weighted Least Squares and Robust Maximum Likelihood Estimation
Bibliographic Data
| ID | 23334166 |
|---|---|
| Authors | Deborah L Bandalos (0000-0003-4839-8188, James Madison University, corresponding author) |
| Year | 2014 |
| Volume | 21 |
| Issue | 1 |
| Pages | 102-116 |
| Publication date | 2014-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2014.859510 |
| OpenAlex | W2085399079 |
| Language | EN |
| Citations received | 95 |
| References cited | 22 |
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.
Categorical variable · Estimation theory · Estimator · Least-squares function approximation · Maximum likelihood · Sample size determination · Standard deviation · Standard error · Statistics · Advanced Statistical Methods and Models · Bayesian Modeling and Causal Inference · Mathematics · Statistical Methods and Bayesian Inference
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| Unique citing works | 95 |
|---|---|
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