How to design a panel study for investigating bivariate dynamics using continuous-time modeling

Designing effective longitudinal panel studies to capture bivariate dynamics requires making informed decisions about how many measurements to collect and when to collect them. This study examines how sample size, number of repeated measures, and various aspects of the temporal sampling schedule influence the recovery of bivariate dynamic parameters in panel data analyzed using a continuous-time random-intercept cross-lagged panel model (CT-RI-CLPM). Through a Monte Carlo simulation, we tested a range of population generating conditions. We also proposed a novel approach for planning sampling schedules: the short-and-long-lags design (SHALL), which deliberately combines closely spaced and widely spaced intervals. SHALL schedules are simple to implement, require no prior knowledge of the system dynamics, and take advantage of the capacity of continuous-time models to accommodate unequal time intervals. Our findings indicate that the number of measurement occasions and sample size, rather than the temporal distribution of the measures, are the primary determinants of parameter recovery. Cross-effects proved particularly difficult to recover, exhibiting low estimation efficiency and statistical power even under the most favorable conditions. We provide actionable recommendations for researchers planning panel data studies, along with open-source R code to facilitate the design of SHALL sampling schedules and for fitting the CT-RI-CLPM to new data sets. An empirical example illustrates the application of such model to data from an empirical panel study relating self-esteem and depression. (PsycInfo Database Record (c) 2026 APA, all rights reserved).