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Two important applications of Scale Adjusted Latent Class (SALC) models are:
In step 2, classification errors are introduced when assigning individuals to latent classes. The estimates of the association with the external variables need to be corrected for classification errors to prevent a downward bias (Bolck, Croon, and Hagenaars, 2004). The Step3 module implements two bias adjustments procedures (Vermunt, 2010).
You can use informative diagnostic statistics to see whether multiple models are needed.
Latent class (LC) choice models analyze these data in a way that accounts for heterogeneity by allowing different population segments (latent classes) to express different preferences in making their choices.
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