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• W2896838285 abstract "In applications of structural equation modeling, the assumptions of correct model and normal data for maximum likelihood (ML) estimation almost never hold. Currently, the most common solution is to use a sandwich covariance estimator that is robust to (a) nonnormal data only (denoted as MLM), or (b) nonnormal data and incorrect model (denoted as MLR). In this paper, we propose new methods that perform even better than do MLM and MLR given model misfit, nonnormal data, and sample sizes typical of the behavioral sciences. In particular, we propose one standard error estimator and two confidence interval methods. The new methods also have analytic formulas and are thus light on computation burden. We first use statistical reasoning to show the new methods are superior to MLM and MLR, and then carry out simulations to compare ML, MLM, MLR, and the new methods. The simulation results confirm the superiority of the proposed methods." @default.
• W2896838285 created "2018-10-26" @default.
• W2896838285 creator A5079715723 @default.
• W2896838285 date "2018-10-15" @default.
• W2896838285 modified "2023-10-17" @default.
• W2896838285 title "More Robust Standard Error and Confidence Interval for SEM Parameters Given Incorrect Model and Nonnormal Data" @default.
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• W2896838285 doi "https://doi.org/10.1080/10705511.2018.1505522" @default.
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