Measurement and Structural Model Class Separation in Mixture CFA: ML/EM Versus MCMC
- 17 April 2012
- journal article
- research article
- Published by Informa UK Limited in Structural Equation Modeling: A Multidisciplinary Journal
- Vol. 19 (2), 178-203
- https://doi.org/10.1080/10705511.2012.659614
Abstract
Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the EM algorithm was compared to a Markov chain Monte Carlo (MCMC) estimator condition using weak priors and a condition using tight priors. Results indicated that the MCMC weak condition produced the highest bias, particularly with a weak Dirichlet prior for the mixture class proportions. Specifically, the weak Dirichlet prior affected parameter estimates under all mixture class separation conditions, even with moderate and large sample sizes. With little knowledge about parameters, ML/EM should be used over MCMC weak. However, MCMC tight produced the lowest bias under all mixture class separation conditions and should be used if tight and accurate priors can be placed on parameters.Keywords
This publication has 15 references indexed in Scilit:
- Evaluation of Structural Equation Mixture Models: Parameter Estimates and Correct Class AssignmentStructural Equation Modeling: A Multidisciplinary Journal, 2010
- The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture ModelsStructural Equation Modeling: A Multidisciplinary Journal, 2008
- How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGSStatistics in Medicine, 2005
- Investigating population heterogeneity with factor mixture models.Psychological Methods, 2005
- Experiences With Markov Chain Monte Carlo Convergence Assessment in Two Psychometric ExamplesJournal of Educational and Behavioral Statistics, 2004
- General Methods for Monitoring Convergence of Iterative SimulationsJournal of Computational and Graphical Statistics, 1998
- Finite-Mixture Structural Equation Models for Response-Based Segmentation and Unobserved HeterogeneityMarketing Science, 1997
- An introduction to finite mixture distributionsStatistical Methods in Medical Research, 1996
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992
- Explaining the Gibbs SamplerThe American Statistician, 1992