Psychological research typically focuses on either traits-that is, relatively stable characteristics of individuals-or processes. The latter, by definition, imply some form of change over time. Roughly, we can distinguish between two kinds of processes: developmental processes and stable processes. Developmental processes such as growth, learning, maturation, and decline, typically take place over relatively long periods of time (e.g., years or decades), and the changes that are associated with developmental processes are often well described by smooth trends (e.g., linear trends or logistic curves), or by sudden shifts (as when a certain ability is suddenly acquired or lost). In contrast, stationary processes are characterized by reversible changes over time, and tend to take place at relatively small time scales. Examples are the momentary fluctuations in affect and the reciprocal influence of partners during a conversation.
The Dynamic Modeling Lab was founded to develop statistical techniques that can be used to study such stable processes and to relate them to developmental processes. As a general strategy our point of departure are time series models that have proven their value for modeling psychological processes at the level of the individual in single subject research (see the video below for a presentation of time series analysis in psychological research). These models are then used as building blocks in novel dynamic multilevel models. This approach ensures that we can study individual differences in process features and simultaneously capture the basic generalities that characterize the process under investigation. Specifically, we are interested in the individual differences in dynamics, which reflect individual differences in for instance regulatory strength, coping strategies, and psychophysiological interactions.