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Latent GOLD® implements the more general mixture Latent Markov model where different latent classes are allowed to have different transition probabilities.
The latent Markov model is a popular longitudinal data variant of the standard latent class model; it is in fact a latent class cluster model in which individuals are allowed to switch between clusters across measurement occasions.
Latent GOLD® improves over traditional ad-hoc types of cluster analysis methods by including model selection criteria and probability-based classification. Posterior membership probabilities are estimated directly from the model parameters and used to assign cases to the classes.
The adjacent-category ordinal logit model is used for situations where the response data consists of ratings as opposed to choices.
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