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- W2074723722 abstract "Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue, especially when the assumption of proper model specification is violated. The current simulation study compared the performance of a linear growth mixture model (GMM) for determining the correct number of latent classes against a completely unrestricted multivariate normal mixture model. Results revealed that model convergence is a serious problem that has been underestimated by previous GMM studies. Based on two ways of dealing with model nonconvergence, the performance of the two types of mixture models and a number of model fit indices in class identification are examined and discussed. This article provides suggestions to practitioners who want to use GMM for their research." @default.
- W2074723722 created "2016-06-24" @default.
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- W2074723722 date "2014-02-27" @default.
- W2074723722 modified "2023-10-16" @default.
- W2074723722 title "Unrestricted Mixture Models for Class Identification in Growth Mixture Modeling" @default.
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- W2074723722 doi "https://doi.org/10.1177/0013164413519798" @default.
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