This is the first presentation in a series of two presentations on continuous time dynamic modeling. Continuous time dynamic modeling is an approach for the analysis of change that makes optimal use of the time structure to infer the development and dynamic relationships among constructs of interest. In this presentation I will provide a step-by-step introduction to the basics of continuous time dynamic modeling. I will highlight the possibility to work with different data structures including panel data (T small, N large) as well as time series and intensive longitudinal data (N small, T large). Special emphasis will be put on the flexibility of the approach and on how it can help to overcome various limitations of popular alternative approaches for the analysis of change. I will end with an overview of recent developments, current limitations, and future research directions.

How we choose to represent phenomena of interest in the form of models has important implications for the inferences we can sensibly draw from the models and apply back to the phenomena of interest. I will discuss how common approaches for longitudinal modelling may be inadequate for scientific inference, argue that stochastic differential equations may provide a more appropriate basis for such inference, and present the ctsem software for hierarchical continuous time state-space modelling. Along the way I will show how complex forms of heterogeneity across subjects and time may be handled, discuss the modelling of developmental phenomena, the inclusion of intervention effects in a systems model, and present a likely naive but hopeful viewpoint for directions that a serious approach to dynamic systems thinking could take us.