In the field of motivation science, we deal with many different types of longitudinal data (e.g., panel data, intensive longitudinal data, time-series data from behavioral experiments and neuroimaging). Despite the popularity, we still do not have a good idea to analyze such data given a specific research question. We aim to understand how applied researchers can appropriately choose the right methods to analyze longitudinal data.
Key words: Statistical simulation, mixed-effects modeling, structural equation modeling
Usami, S., Todo, N., & Murayama, K. (2019). Modeling reciprocal effects in medical research: Critical discussion on the current practices and potential alternative models. PLoS ONE, 14(9), Article e0209133. PDF Online Article