Longitudinal or panel data analysis refers to the statistical analysis of pooled data which consists of a cross-section of units (e.g., countries, firms, households, individuals) for which there exist repeated observations over time. Many of the longitudinal data applications that appear in the literature are based on linear model theory. The most prominent linear panel data models are the fixed-effects model, and the random-effects model, both of which are applied to model the level of the dependent variable. As to the most prominent approaches in order to model the change of the dependent variable, the lagged dependent variable approach, and the change score method are most frequently utilized. Although panel data offer many advantages to study causal propositions, the power of the analysis largely depends on the assumptions inherent in the statistical models.
