The increasing use of diary methods calls for the development of appropriate statistical methods.
For the resulting panel data, latent Markov models can be used to model both individual differences
and temporal dynamics. The computational burden associated with these models can be overcome by
exploiting the conditional independence relations implied by the model. This is done by associating a
probabilistic model with a directed acyclic graph, and applying transformations to the graph. The structure
of the transformed graph provides a factorization of the joint probability function of the manifest and latent
variables, which is the basis of a modified and more efficient E-step of the EMalgorithm. The usefulness of
the approach is illustrated by estimating a latent Markov model involving a large number of measurement
occasions and, subsequently, a hierarchical extension of the latent Markov model that allows for transitions
at different levels. Furthermore, logistic regression techniques are used to incorporate restrictions on the
conditional probabilities and to account for the effect of covariates. Throughout, models are illustrated
with an experience sampling methodology study on the course of emotions among anorectic patients.