The comments on D. J. Bauer and P. J. Curran (2003) share 2 common themes. The 1st theme is that model-checking procedures may be capable of distinguishing between mixtures of normal and homogeneous nonnormal distributions. Although useful for assessing model quality, it is argued here that currently available procedures may not always help discern between these 2 possibilities. The 2nd theme is that even if these 2 possibilities cannot be distinguished, a growth mixture model may still provide useful insights into the data. It is argued here that whereas this may be true for the scientific goals of description and prediction, the acceptance of a model that fundamentally misrepresents the underlying data structure may be less useful in pursuit of the goal of explanation.
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