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• W2954163905 abstract "Recent work reframes direct effects of covariates on items in mixture models as differential item functioning (DIF) and shows that, when present in the data but omitted from the fitted latent class model, DIF can lead to overextraction of classes. However, less is known about the effects of DIF on model performance—including parameter bias, classification accuracy, and distortion of class-specific response profiles—once the correct number of classes is chosen. First, we replicate and extend prior findings relating DIF to class enumeration using a comprehensive simulation study. In a second simulation study using the same parameters, we show that, while the performance of LCA is robust to the misspecification of DIF effects, it is degraded when DIF is omitted entirely. Moreover, the robustness of LCA to omitted DIF differs widely based on the degree of class separation. Finally, simulation results are contextualized by an empirical example." @default.
• W2954163905 created "2019-07-12" @default.
• W2954163905 creator A5013467630 @default.
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• W2954163905 date "2019-07-02" @default.
• W2954163905 modified "2023-10-13" @default.
• W2954163905 title "Assessing the Robustness of Mixture Models to Measurement Noninvariance" @default.
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• W2954163905 doi "https://doi.org/10.1080/00273171.2019.1596781" @default.
• W2954163905 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7247772" @default.
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• W2954163905 hasPublicationYear "2019" @default.
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