Abstract: The field of "dynamics" has gained significant popularity in longitudinal research in recent years. This approach examines the temporal effects of psychological constructs through multiple repeated measurements, with a particular focus on reciprocal effects. With advancements of data collection methods, i.e., intensive longitudinal data, issues on dynamics can be analyzed under two time scales: discrete time and continuous time. The present study uses unified modeling parameters to review the modeling methods and applicable conditions of commonly used statistical models within both discrete-time and continuous-time scales in intensive longitudinal data. An empirical example from a diary study is provided to compare these two scaling methods. The relationships between the two scaling models are compared and discussed in detail, addressing issues such as reparameterization, challenges related to violating the equal time interval assumption, potential solutions to these challenges, and factors influencing parameter estimation. The present study serves as a reference for model selection for researchers in dynamics research.

Key words: dynamics, intensive longitudinal data, discrete time, continuous time