Measuring change: Mixed Markov models for ordinal panel data

In an analysis of longitudinal data it is important to distinguish between dependencies caused by within-and between-subject variability. This paper presents mixed Markov models for ordinal data that take into account both sources of variation. In addition, covariates that may capture differences among panel members and time-specific changes are also incorporated in the model. The model is derived by specifying an observation-driven process at the individual level and allowing for parametric or semi-parametric representations of random parameter variation across the units of analysis. As a result, the approach is well suited for modelling ordinal panel data with a large number of time points. In an application a three-week diary study is analysed to test hypotheses about the relationships between emotions and personality factors over time.