Continuous versus discrete time
This is the landing page for topics related to modeling your intensive longitudinal data using discrete versus continuous time models.
Choosing between discrete and continuous time models affects how time is conceptualized and how temporal processes are represented. Continuous time models assume the process evolves continuously over time, whereas discrete time models treat time as happening in regular discrete steps. This distinction is crucial because it influences model structure, interpretation of dynamics, and the types of research questions that can be appropriately addressed.
Below we have specified multiple articles where you can read more about discrete and continuous time modeling.
Think more about discrete versus continuous time
Continuous time models can handle irregular spacing and model change as a smooth process.
- [Continuous time model versus perspective]
- Continuous time modeling
- [Ornstein–Uhlenbeck (OU) process]
- [Coupled oscillators]
Discrete time models are suited for data collected in fixed time steps.
- [Autoregressive models]
- [Dynamic structural equation models]