To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals. In the present paper, we examine how continuous time dynamic models can be used to study nonexperimental intervention effects in longitudinal studies where measurement intervals vary between and within participants. We empirically illustrate this method by using panel data (N = 2,877) to study the effect of the transition from primary to secondary school on students' motivation. Results of a simulation study also show that the precision and recovery of the estimate of the effect improves with individual variation in time intervals.