Bayesian analysis of multivariate latent curve models with nonlinear longitudinal latent effects

Structural Equation Modeling

Volumn 16, Issue 2, 2009, Pages 245-266

  Song, Xin Yuan ^a   Lee, Sik Yum ^a   Hser, Yih Ing ^b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; DYNAMIC CHANGES; INDIVIDUAL DIFFERENCES; INTERACTION RELATIONSHIP; INTERACTIVE EFFECT; LONGITUDINAL STUDY; MEDICAL RESEARCH; ORDERED CATEGORICAL DATA;

STATISTICAL METHODS;

MULTIVARIANT ANALYSIS;

EID: 67650696829     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705510902751275     Document Type: Article

References (32)

1. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.
2. Ansari, A., & Jedidi, K. (2000). Bayesian factor analysis for multilevel binary observations. Psychometrika, 65, 475-498.
3. Ansari, A., Jedidi, K., & Jagpal, S. (2000). A hierarchical Bayesian methodology for treating heterogeneity in structural equation models. Marketing Science, 19, 328-347.
4. Blozis, S. A. (2007). On fitting nonlinear latent curve models to multiple variables measured longitudinally. Structural Equation Modeling, 14(2), 179-201.
5. Blozis, S. A., & Cho, Y. (2007). Coding and centering of time in latent curve models in the presence of interindividual time heterogeneity. Structural Equation Modeling, 15(3), 413-433.
6. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley.
7. Browne, R. A., Monti, P. M., Myers, M. G., Martin, R. A., Rivinus, T., Dubreuil, M. E. T., et al. (1998). Depression among cocaine abusers in treatment: Relation to cocaine and alcohol use and treatment outcome. American Journal of Psychiatry, 155, 220-225.
8. Cowles, M. K. (1996). Accelerating Monte Carlo Markov chain convergence for cumulative link generalized linear models. Statistics and Computing, 6, 101-111.
9. Dunson, D. B. (2000). Bayesian latent variable models for clustered mixed outcomes. Journal of the Royal Statistical Society, Series B, 62, 355-366.
10. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). London: Chapman & Hall.
11. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
12. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their application. Biometrika, 57, 97-109.
13. Hser, Y., Stark, M. E., Paredes, A., Huang, D., Anglin, M. D., & Rawson, R. (2006). A 12-year follow-up of a treated cocaine dependent sample. Journal of Substance Abuse Treatment, 30, 219-226.
14. Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239-251.
15. Jöreskog, K. G., & Sörbom, D. (1996). LISREL 8: Structural equation modeling with the SIMPLIS command language. Hove, UK: Scientific Software International.
16. Jöreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 57-88). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
17. Kasarabada, D. N., Anglin, M. D., Khalsa-Denison, E., & Paredes, A. (1999). Differential effects of treatment modality on psychosocial functioning of cocaine-dependent men. Journal of Clinical Psychology, 55, 257-274.
18. Lee, S. Y. (2007). Structural equation modeling: A Bayesian approach. Chichester, UK: Wiley.
19. Lee, S. Y., & Song, X. Y. (2003). Model comparison of nonlinear structural equation models with fixed covariates. Psychometrika, 68, 27-47.
20. Lee, S. Y., & Song, X. Y. (2004). Evaluation of the Bayesian and maximum likelihood approaches in analyzing structural equation models with small sample sizes. Multivariate Behavioral Research, 39, 653-686.
21. Lee, S. Y., Song, X. Y., & Poon, W. Y. (2004). Comparison of approaches in estimating interaction and quadratic effects of latent variables. Multivariate Behavioral Research, 39, 37-67.
22. Lee, S. Y., Song, X. Y., Skevington, S., & Hao, Y. T. (2005). Application of structural equation models to quality of life. Structural Equation Modeling, 12, 435-453.
23. Lee, S. Y., Song, X. Y., & Tang, N. S. (2007). Bayesian methods for analyzing structural equation models with covariates, interaction, and quadratic latent variables. Structural Equation Modeling, 14, 404-434.
24. Li, F. Z., Duncan, T. E., & Acock, A. (2000). Modeling interaction effects in latent growth curve models. Structural Equation Modeling, 7, 497-533.
25. Lindley, D. V., & Smith, A. F. M. (1972). Bayes estimates for the linear model (with discussion). Journal of the Royal Statistical Society, Series B, 34, 1-42.
26. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
27. Liu, H., & Powers, D. A. (2007). Growth curve models for zero-inflated count data: An application of smoking behavior. Structural Equation Modeling, 14(2), 247-279.
28. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
29. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equations of state calculations by fast computing machine. Journal of Chemical Physics, 21, 1087-1091.
30. Shi, J. Q., & Lee, S. Y. (2000). Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society, Series B, 62, 77-87.
31. Sturtz, S., Ligges, U., & Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software, 12, 1-16.
32. Zellner, A. (1971). An introduction to Bayesian inference in econometrics. New York: Wiley.