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Original Articles

Building Latent Class Growth Trees

REFERENCES

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723. doi:10.1109/TAC.1974.1100705
  • Crayen, C., Eid, M., Lischetzke, T., Courvoisier, D. S., & Vermunt, J. K. (2012). Exploring dynamics in mood regulation: Mixture latent Markov modeling of ambulatory assessment data. Psychosomatic Medicine, 74, 366–376. doi:10.1097/PSY.0b013e31825474cb
  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39, 1–38.
  • Elliott, D.S., Huizinga, D., and Menard, S.(1989). Multiple problem youth: Delinquency, substance use, and mental health problems. New York: Springer-Verlag
  • Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Hierarchical clustering. In Cluster analysis (5th ed., pp. 71–110). Chichester: John Wiley & Sons.
  • Francis, B., Elliott, A., & Weldon, M. (2016). Smoothing group-based trajectory models through b-splines. Journal of Developmental and Life-Course Criminology, 2, 113–133. doi:10.1007/s40865-016-0025-6
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1). Berlin, Germany: Springer.
  • Ghattas, B., Michel, P., & Boyer, L. (2017). Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods. Pattern Recognition, 67, 177–185. doi:10.1016/j.patcog.2017.01.031
  • Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. New York, NY: Guilford.
  • Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research, 29, 374–393. doi:10.1177/0049124101029003005
  • Jung, T., & Wickrama, K. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302–317. doi:10.1111/j.1751-9004.2007.00054.x
  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778. doi:10.1093/biomet/88.3.767
  • MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–226. doi:10.1146/annurev.psych.51.1.201
  • McLachlan, G., & Peel, D. (2004). Finite mixture models. New York, NY: Wiley.
  • Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8, 384–393.
  • Muthén, B. (2004). Latent variable analysis. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 345–368). Thousand Oaks, CA: Sage Publication .
  • Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.
  • Nagin, D. S., & Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31, 327–362. doi:10.1111/crim.1993.31.issue-3
  • Nesselroade, J. R. (1991). Interindividual differences in intraindividual change. In L. M. Collins & J. L Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 92–105). Washington, DC: American Psychological Association.
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (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. doi:10.1080/10705510701575396
  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). Thousand Oaks, CA: Sage.
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136
  • Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343. doi:10.1007/BF02294360
  • Tofighi, D., & Enders, C. K. (2008). Identifying the correct number of classes in growth mixture models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 317–341). Greenwich, CT: Information Age.
  • Van De Schoot, R., Sijbrandij, M., Winter, S. D., Depaoli, S., & Vermunt, J. K. (2017). The GRoLTS-checklist: Guidelines for reporting on latent trajectory studies. Structural Equation Modeling, 24, 451–467.
  • van Den Bergh, M., Schmittmann, V. D., & Vermunt, J. K. (2017). Building latent class trees, with an application to a study of social capital. Methodology,13, 13–22.
  • van Der Palm, D. W., Van der Ark, L. A., & Vermunt, J. K. (2016). Divisive latent class modeling as a density estimation method for categorical data. Journal of Classification, 33(1), 52–72.
  • Vermunt, J. K. (2007). Growth models for categorial response variables: Standard, latent-class, and hybrid approaches. In K. Van Montfort, H. Oud, & A. Satorra (Eds.), Longitudinal models in the behavioral and related sciences (pp. 139–1580). Mahwah, NJ: Erlbaum.
  • Vermunt, J. K., & Magidson, J. (2013). Technical guide for Latent GOLD 5.0: Basic, advanced, and syntax. Belmont, MA: Statistical Innovations Inc.