Measurement and Structural Model Class Separation in Mixture CFA: ML/EM Versus MCMC

Structural Equation Modeling

Volumn 19, Issue 2, 2012, Pages 178-203

  Depaoli, Sarah ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Bayesian estimation; class separation; confirmatory factory analysis; finite mixture models; parameter recovery

Indexed keywords

EID: 84861637571     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2012.659614     Document Type: Article

References (28)

1. Asparouhov, T. and Muthén, B. O. 2010. Bayesian analysis using Mplus Unpublished manuscript
2. Bollen, K. A. and Curran, P. J. 2006. Latent curve models: A structural equation perspective. New York, NY: Wiley.
3. Brooks, S. P. and Gelman, A. 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7: 434-455.
4. Casella, G. and George, E. I. 1992. Explaining the {G}ibbs sampler. The American Statistician, 46: 167-174.
5. Diebolt, J. and Robert, C. P. 1994. Estimation of finite mixture distributions through {B}ayesian sampling. Journal of the Royal Statistical Society, 56: 363-375.
6. Enders, C. K. and Tofighi, D. 2008. The impact of misspecifying class-specific residual variances in growth mixture models. Structural Equation Modeling, 15: 75-95.
7. Everitt, B. S. 1996. An introduction to finite mixture distributions. Statistical Methods in Medical Research, 5: 107-127.
8. Gelman, A. 1996. "Inference and monitoring convergence.". In Markov chain {Monte {C}arlo in practice} Edited by: Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. 131-143. New York, NY: Chapman & Hall.
9. Gelman, A. and Rubin, D. B. 1992a. Inference from iterative simulation using multiple sequences. Statistical Science, 7: 457-511.
10. Gelman, A. and Rubin, D. B. 1992b. "A single series from the {G}ibbs sampler provides a false sense of security.". In Bayesian Statistics 4 Edited by: Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M. 625-631. Oxford, UK: Oxford University Press.
11. Geman, S. and Geman, D. 1984. Stochastic relaxation, {G}ibbs distributions and the {B}ayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6: 721-741.
12. Geyer, C. J. 1991. "Markov chain {M}onte {C}arlo maximum likelihood.". In Computing science and statistics: Proceedings of the 23rd Symposium on the Interface 156-163. Retrieved fromhttp://purl.umn.edu/58440
13. Ghosh, J. K. and Mukerjee, R. 1992. "Non-informative priors (with discussion).". In Bayesian statistics 4 Edited by: Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M. 195-210. Oxford, UK: Oxford University Press.
14. Jedidi, K., Jagpal, H. S. and DeSarbo, W. S. 1997. Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16: 39-59.
15. Lambert, P. C., Sutton, A. J., Burton, P. R., Abrams, K. R. and Jones, D. R. 2005. How vague is vague? {A} simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Statistics in Medicine, 24: 2401-2428.
16. Lee, S.-Y. 2007. Structural equation modeling: A Bayesian approach. Chichester, UK: Wiley.
17. Lubke, G. H. and Muthén, B. O. 2005. Investigating population heterogeneity with factor mixture models. Psychological Methods, 10: 21-39.
18. Lunn, D. J., Thomas, A., Best, N. and Spiegelhalter, D. 2000. WinBUGS-A Bayesian modeling framework: Concepts, structure, and extensibility. Statistics and Computing, 10: 325-337.
19. McLachlan, G. and Peel, D. 2000. Finite mixture models. New York, NY: Wiley.
20. Muthén, B. O. 2004. "Latent variable analysis: {G}rowth mixture modeling and related techniques for longitudinal data.". In Handbook of quantitative methodology for the social sciences Edited by: Kaplan, D. 345-368. Newbury Park, CA: Sage.
21. Muthén, B. O. and Shedden, K. 1999. Finite mixture modeling with mixture outcomes using the {EM} algorithm. Biometrics, 55: 463-469.
22. Muthén, L. K. and Muthén, B. O. 1998-2010. Mplus user's guide, 6th ed. Los Angeles, CA: Muthén & Muthén.
23. Natarajan, R. and Mc{C}ulloch, C. E. 1998. Gibbs sampling with diffuse proper priors: {A} valid approach to data-driven inference?. Journal of Computational and Graphical Statistics, 7: 267-277.
24. R Development Core Team. 2008. R: A language and environment for statistical computing [Computer software manual] Vienna, Austria Retrieved fromhttp://www.R-project.org
25. Sinharay, S. 2004. Experiences with {M}arkov chain {M}onte {C}arlo convergence assessment in two psychometric examples. Journal of Educational and Behavioral Statistics, 29: 461-488.
26. Tofighi, D. and Enders, C. K. 2008. "Identifying the correct number of classes in growth mixture models.". In Advances in latent variable mixture models Edited by: Hancock, G. R. and Samuelson, K. M. 317-341. Charlotte, NC: Information Age.
27. Tolvanen, A. 2008. Latent growth mixture modeling: {A simulation study} (Unpublished doctoral dissertation) Finland: University of Jyvaskyla.
28. Tueller, S. and Lubke, G. 2010. Evaluation of structural equation mixture models: {P}arameter estimates and correct class assignment. Structural Equation Modeling, 17: 165-192.