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Johan Lyrvall; Zsuzsa Bakk; Jennifer Oser; Roberto Di Mari – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Simulation
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Julian F. Lohmann; Steffen Zitzmann; Martin Hecht – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…
Descriptors: Time Management, Behavioral Science Research, Predictive Validity, Predictor Variables
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Liu, Siwei; Rovine, Michael J.; Molenaar, Peter C. M. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error…
Descriptors: Structural Equation Models, Goodness of Fit, Longitudinal Studies, Data Analysis