Novel Full-Bayesian and Hybrid-Bayesian Approaches for Modeling Intraindividual Variability

Intraindividual variability (IIV) characterizes the amplitude and temporal dependency of short-term fluctuations of a variable and is often used to predict outcomes in psychological studies. However, how to properly model IIV is understudied. In particular, intraindividual standard deviation (or variance), which quantifies the amplitude of fluctuation of a variable around its mean level, can be challenging to model directly in popular latent variable frameworks, such as dynamic structural equation modeling (DSEM). In this study, we introduced three novel modeling methods, including two two-step hybrid-Bayesian methods using DSEM and a one-step full Bayesian method, to model IIV as predictors. We conducted a simulation study to evaluate the performance of the three methods and compared their performance to that of the conventional regression approach under various data conditions. Simulation results showed that the hybrid-Bayesian approach with multiple draws (HBM) and the one-step full Bayesian (FB) approach performed well in recovering the parameters when sufficient sample size and time points were available. The data requirement of using FB was lower than HBM. However, the conventional approach and hybrid-Bayesian approach with a single draw failed to recover parameters, even with large samples. We provided a simulated data example with code online to illustrate the use of the methods.