Factor mixture models combine a categorical representation of the data with dimensional variation within each class [44]. The categorical latent variable identifies distinct groups in the population, and the dimensional latent variable (or the factor) is used to describe a continuum that exists within each class, for example, a severity continuum [28]. Models can have different numbers of classes and factors. In this analysis, factor mixture models with one to three classes were run. Conceptually, the 1 factor model can also be thought of as a 1 class, 1 factor model. To avoid confusion, the model will be referred to herein as a 1 class, 1 factor model. Models with one to three classes and one factor were tested: no further classes were added as the 3 class, 1 factor model was an inferior fit compared to the models with fewer classes.
