Since both types of clustered structures contribute to the dependency in the data, both aspects need to be taken into account when modeling such data. Many developments for multilevel and longitudinal data have focused on continuous response or outcome variables, and less attention has been paid to data with discrete manifest and latent variables.

The Multilevel Latent Markov Model (MLMM) extends the latent class model to simultaneously incorporate the temporal and structural dependency in a single model.

A data set from Educational Longitudinal Study of 2002 is used to illustrate various models for discrete longitudinal data with multilevel data structure.

date and location