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Measurement and Uncertainty Preserving Parametric Modeling for Continuous Latent Variables with Discrete Indicators and External Variables
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Roy Levy; Daniel McNeish – Journal of Educational and Behavioral Statistics, 2025
Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…
Descriptors: Models, Statistical Analysis, Measurement, Data Interpretation
Direct Discrepancy Dynamic Fit Index Cutoffs for Arbitrary Covariance Structure Models
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Daniel McNeish; Melissa G. Wolf – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Despite the popularity of traditional fit index cutoffs like RMSEA [less than or equal to] 0.06 and CFI [greater than or equal to] 0.95, several studies have noted issues with overgeneralizing traditional cutoffs. Computational methods have been proposed to avoid overgeneralization by deriving cutoffs specifically tailored to the characteristics…
Descriptors: Structural Equation Models, Cutting Scores, Generalizability Theory, Error of Measurement