SMABS 2004 Jena University
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European Association of Methodology

Department of methodology and evaluation research

Jena University

Contributions: Abstract

This one-day workshop describes new modeling opportunities using latent variable techniques that are available in Version 3 of the Mplus program (

Mplus is an easy to use program, which is built on a general modeling framework that achieves its flexibility from using a combination of categorical and continuous latent variables. This framework gives as special cases structural equation modeling (SEM), growth modeling, mixture (latent class) modeling, and multilevel modeling. The framework fully integrates these types of models to provide many new model extensions. Mplus Version 3 is even more powerful and easy to use, adding many unique modeling, algorithmic, and model testing features for SEM, mixture modeling, and multilevel modeling, as well as a simplified growth modeling language, graphical output of results such as estimated growth mixture curves, and automatically generated starting values with random perturbations for exploratory latent class analysis and growth mixture modeling.

New features in the area of SEM include maximum-likelihood (ML) regression, path analysis, factor analysis, and growth modeling with censored, categorical, and zero-inflated Poisson count outcomes, including missing data; latent variable interaction modeling and non-linear factor analysis using ML; and non-linear parameter constraints. New features in the area of mixture (latent class) modeling include modeling with censored, nominal, and zero-inflated Poisson outcomes; confirmatory latent class analysis with multiple latent class variables and covariates such as in latent transition mixture analysis and hidden Markov mixture modeling; latent class loglinear modeling including loglinear modeling with missing data; latent class analysis with random effects; and factor mixture modeling with categorical outcomes. New features in the area of multilevel modeling include two-level SEM and growth modeling with categorical outcomes using ML, fully integrated ML treatment of random effects and factors for both individual and cluster variates, three-level modeling of longitudinal data on categorical outcomes, two-level latent class analysis, and three-level growth mixture modeling.

The workshop illustrates these methods and provides Mplus input and output using examples of growth modeling with latent variable interaction mediators, estimation of treatment effects with non-compliance and placebo response in randomized trials, prediction of high school dropout and reading failure using growth mixture modeling, non-ignorable missing data modeling, discrete-time survival mixture modeling with frailties and two-level data, non-parametric representation of random effect distributions, and twin modeling with genetic factors underlying diagnostic criteria.