Abstract
The article presents applications of different growth mixture models consider- ing unobserved heterogeneity within the framework of Mplus (Muthand Muth´ 2001, 2004). Latent class growth mixture models are discussed under special con- sideration of count variables which can be incorporated into the mixture models via the Poisson and the zero-inflated Poisson model. Four-wave panel data from a Ger- man criminological youth study (Boers et al., 2002) is used for the model analyses. Three classes can be obtained from the data: Adolescents with almost no deviant and delinquent activities, a medium proportion of adolescents with a low increase of delinquency and a small number with a larger growth starting on a higher level. The best model fits are obtained with the zero-inflated Poisson model. Linear growth specifications are almost sufficient. The conditional application of the mixture mod- els includes gender and educational level of the schools as time-independent predic- tors which are able to explain a large proportion of the latent class distribution. The stepwise procedure from latent class growth analysis to growth mixture modeling is feasible for longitudinal analyses where individual growth trajectories are heteroge- nous even when the dependent variable under study cannot be treated as a continuous variable.
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