Sample Size Requirements in Single- and Multiphase Growth Mixture Models: A Monte Carlo Simulation Study

Structural Equation Modeling

Volumn 19, Issue 3, 2012, Pages 457-476

  Kim, Su Young ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

growth mixture modeling; longitudinal data analysis; multiphase longitudinal data; sample size; stage sequential models

Indexed keywords

EID: 84864690386     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2012.687672     Document Type: Article

References (29)

1. Bauer, D. J. and Curran, P. J. 2003. Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8: 338-363.
2. Bollen, K. A. and Curran, P. J. 2006. Latent curve models: A structural equation modeling perspective. Hoboken, NJ: Wiley.
3. Clogg, C. C. 1981. Latent structure models of mobility. American Journal of Sociology, 86: 836-868.
4. Clogg, C. C. 1995. "Latent class models.". In Handbook of statistical modeling for the social and behavioral sciences, Edited by: Arminger, G., Clogg, C. C. and Sobel, M. E. 311-359. New York, NY: Plenum.
5. Duncan, T. E., Duncan, S. C., Strycker, L. A., Okut, H. and Li, F. 2002. Growth mixture modeling of adolescent alcohol use data. Retrieved from http://people.oregonstate.edu/~acock/growth-curves/mixture1-30-02.pdf.
6. Hix-Small, H., Duncan, T. E., Duncan, S. C. and Okut, H. 2004. A multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use. Journal of Psychopathology and Behavioral Assessment, 26: 255-270.
7. Jackson, D. L. 2001. Sample size and number of parameter estimates in maximum likelihood confirmatory factor analysis: A Monte Carlo investigation. Structural Equation Modeling, 8: 205-223.
8. Jackson, D. L. 2007. The effect of the number of observations per parameter in misspecified confirmatory factor analytic models. Structural Equation Modeling, 14: 48-76.
9. Kim, S.-Y. and Kim, J.-S. 2012. Investigating stage-sequential growth mixture models with multiphase longitudinal data. Structural Equation Modeling, 19: 293-319.
10. Li, F., Duncan, T. E., Duncan, S. C. and Hops, H. 2001. Piecewise growth mixture modeling of adolescent alcohol use data. Structural Equation Modeling, 8: 175-204.
11. Loughran, T. and Nagin, D. S. 2006. Finite sample effects in group-based trajectory models. Sociological Methods & Research, 35: 250-278.
12. McCutcheon, A. L. 1987. Latent class analysis Thousand Oaks, CA: Sage.
13. McLachlan, G. and Peel, D. 2000. Finite mixture models New York, NY: Wiley.
14. Meredith, W. and Tisak, J. 1990. Latent curve analysis. Psychometrika, 55: 107-122.
15. Muthén, B. 2001a. "Latent variable mixture modeling.". In New developments and techniques in structural equation modeling, Edited by: Marcoulides, G. and Schumacker, R. 1-33. Mahwah, NJ: Erlbaum.
16. Muthén, B. 2001b. "Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling.". In New methods for the analysis of change, Edited by: Collins, L. and Sayer, A. 291-322. Washington, DC: American Psychological Association.
17. Muthén, B. 2001c. Two-part growth mixture modeling Retrieved from http://gseis.ucla.edu/faculty/muthen/articles/Article_094.pdf.
18. Muthén, B. 2004. "Latent variable analysis: Growth 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.
19. Muthén, B., Khoo, S.-T., Francis, D. J. and Boscardin, C. K. 2003. "Analysis of reading skills development from kindergarten through first grade: An application of growth mixture modeling to sequential processes.". In Multilevel modeling: Methodological advances, issues, and applications, Edited by: Reise, S. R. and Duan, N. 71-89. Mahwah, NJ: Erlbaum.
20. Muthén, B. and Muthén, L. 2000. Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24: 882
21. Muthén, B. and Muthén, L. 2002. How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 4: 599-620.
22. Muthén, B. and Shedden, K. 1999. Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55: 463-469.
23. Muthén, L. and Muthén, B. 2008. Mplus: Statistical analysis with latent variables user's guide 5.0 Los Angeles, CA: Muthén & Muthén.
24. Nagin, D. S. 1999. Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4: 139-157.
25. Nylund, K. L., Asparouhov, T. and Muthén, B. 2007. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14: 535-569.
26. Raudenbush, S. W. and Bryk, A. S. 2002. Hierarchical linear models: Applications and data analysis methods Thousand Oaks, CA: Sage.
27. Stone, A. A. and Shiffman, S. 1994. Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine, 16: 199-202.
28. 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 Samuelsen, K. M. 317-341. Greenwich, CT: Information Age.
29. Walls, T. A. and Schafer, J. L. 2006. Models for intensive longitudinal data New York, NY: Oxford University Press.