Hierarchical Mixtures of Latent Trait Analyzers with concomitant variables

We extend the Mixture of Latent Trait Analyzers (MLTA) with concomitant variables in a multilevel framework to perform a hierarchical clustering of first and second-level units. The use of Mixture of Latent Trait Analyzers allows us to cluster first-level units, while also accounting for the residual variability of items in the data matrix. Furthermore, a multilevel approach allows us to account for the hierarchical structure of the data. Last, the inclusion of concomitant variables enables us understanding how first-level units’ characteristics influence clustering formation.