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Mplus provides both Bayesian and frequentist inference. Bayesian analysis uses Markov chain Monte Carlo (MCMC) algorithms. Posterior distributions can be monitored by trace and autocorrelation plots. Convergence can be monitored by the Gelman-Rubin potential scaling reduction using parallel computing in multiple MCMC chains. Posterior predictive checks are provided.
Bayesian estimation of twolevel models with latent variable interactions using the XWITH option (Asparouhov & Muthén, 2019b). This is especially helpful for models with moderation where maximum-likelihood estimation is problematic due to the need for numerical integration.
Time Series Analysis
Time series analysis is used to analyze intensive longitudinal data such as those obtained with ecological momentary assessments, experience sampling methods, daily diary methods, and ambulatory assessments. Such data typically have a large number of time points, for example, twenty to two hundred.
Three new models have been added for categorical dependent variables: the Three-parameter Logistic Regression Model with a guessing parameter (3PL), the Four-parameter Logistic Regression Model with lower (guessing) and upper asymptote parameters (4PL), and the Partial Credit Model (PCM).
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