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A DFactor model is often used for variable reduction or to define an ordinal attitudinal scale. It contains one or more DFactors which group together variables sharing a common source of variation. Each DFactor is either dichotomous (the default option) or consists of 3 or more ordered levels (ordered latent classes).
The clusters are now called latent states. The Latent Markov model is also referred to as the Latent Transition model.
The traditional latent class model can be used to handle measurement and classification errors in categorical variables, and can accomodate avriables that are nominal, ordinal, continuous, counts, or any combination of these. Covariates can be included directly in the model as well for improved cluster description.
The multilevel option can also be used for specifying three-level parametric or nonparametric random-effects regression models or to develop group-level and individual level segments simultaneously.
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