Over the past decades behavioral scientists have increasingly realized the potential of longitudinal data to address specific research questions, in particular those of a cause-effect nature. As a consequence, the methodology of longitudinal research and analysis has made much progress and an increasing number of large-scale longitudinal (time series and panel) data sets have become available for analysis. However, in accordance with the way longitudinal data are collected, at a restricted number of discrete time points, the statistical analysis is typically based on discrete time models. As argued by the authors in the present book, a series of problems is connected to this type of models, which make their results highly questionable. One main issue is the dependence of discrete time parameter estimates on the chosen time interval in dynamic modeling, which leads to incomparability of results across different observation intervals and, if unaccounted for, may even lead to contradictory conclusions.

Continuous time modeling, in particular by means of differential equations, offers a powerful solution to these problems, yet the use of continuous time models in the behavioral and related sciences such as psychology, sociology, economics, and medicine is still rare. Fortunately, recent initiatives to introduce and adapt continuous time models in a behavioral science context are gaining momentum. The purpose of the book is to assess the state of the art and to bring together the different initiatives. Furthermore, we emphasize the applicability of continuous time methods in applied research and practice.