Latent growth curve models as structural equation models are extensively discussed in various research fields (Curran and Muthen in Am. J. Community Psychol. 27:567-595, 1999; Duncan et al. in An introduction to latent variable growth curve modeling. Concepts, issues and applications, 2nd edn., Lawrence Earlbaum, Mahwah, 2006; Muthen and Muthen in Alcohol. Clin. Exp. Res. 24(6):882-891, 2000a; in J. Stud. Alcohol. 61:290-300, 2000b). Recent methodological and statistical extension are focused on the consideration of unobserved heterogeneity in empirical data. Muthen extended the classic structural equation approach by mixture components, i.e. categorical latent classes (Muthen in Marcouldies, G.A., Sckumacker, R.E. (eds.), New developments and techniques in structural equation modeling, pp. 1-33, Lawrance Erlbaum, Mahwah, 2001a; in Behaviometrika 29(1):81-117, 2002; in Kaplan, D. (ed.), The SAGE handbook of quantitative methodology for the social sciences, pp. 345-368, Sage, Thousand Oaks, 2004). The paper discusses applications of growth mixture models with data on delinquent behavior of adolescents from the German panel study Crime in the modern City (CrimoC) (Boers et al. in Eur. J. Criminol. 7:499-520, 2010; Reinecke in Delinquenzverlaufe im Jugendalter: Empirische Uberprufung von Wachstums- und Mischverteilungsmodellen, Institut fur sozialwissenschaftliche Forschung e.V., Munster, 2006a; in Methodology 2:100-112, 2006b; in van Montfort, K., Oud, J., Satorra, A. (eds.), Longitudinal models in the behavioral and related sciences, pp. 239-266, Lawrence Erlbaum, Mahwah, 2007). Observed as well as unobserved heterogeneity will be considered with growth mixture models. Special attention is given to the distribution of the outcome variables as counts. Poisson and negative binomial distributions with zero inflation are considered in the proposed growth mixture models variables. Different model specifications will be emphasized with respect to their particular parameterizations.
Reinecke J, Seddig D. Growth mixture models in longitudinal research. Advances in Statistical Analysis. 2011;95(4):415-434.
Reinecke, J., & Seddig, D. (2011). Growth mixture models in longitudinal research. Advances in Statistical Analysis, 95(4), 415-434. doi:10.1007/s10182-011-0171-4
Reinecke, J., and Seddig, D. (2011). Growth mixture models in longitudinal research. Advances in Statistical Analysis 95, 415-434.
Reinecke, J., & Seddig, D., 2011. Growth mixture models in longitudinal research. Advances in Statistical Analysis, 95(4), p 415-434.
J. Reinecke and D. Seddig, “Growth mixture models in longitudinal research”, Advances in Statistical Analysis, vol. 95, 2011, pp. 415-434.
Reinecke, J., Seddig, D.: Growth mixture models in longitudinal research. Advances in Statistical Analysis. 95, 415-434 (2011).
Reinecke, Jost, and Seddig, Daniel. “Growth mixture models in longitudinal research”. Advances in Statistical Analysis 95.4 (2011): 415-434.
This data publication is cited in the following publications:
This publication cites the following data publications: