Examining within-person variability of each individual: How should we deal with non-varying individuals?

Researchers have witnessed a rapid increase in attention to within-person dynamic processes. However, individuals with observed zero within-person variability (i.e., non-varying individuals) remain understudied. This study investigates how non-varying individuals affect the estimation of within-person dynamic processes and offers practical recommendations. A motivating example of daily stressors demonstrates that including non-varying individuals can change substantive conclusions for univariate autoregressive models. Two simulation studies further explored how different proportions of non-varying individuals affect parameter estimation. Study 1 (univariate) found that non-varying individuals induce systematic upward bias in autoregressive estimates. Study 2 (bivariate) showed that, although average (fixed-effect) cross-lagged estimates may appear accurate, person-specific cross-lagged effects are systematically distorted, with some overestimated and others underestimated. Within this widely used Gaussian autoregressive modeling framework, we therefore recommend estimating dynamic parameters using a subsample that excludes non-varying individuals. We also indicate when subsample estimates can be interpreted as approximations to the full population, versus when they should be interpreted as describing the subsample population (e.g., when the proportion of non-varying individuals reaches around 10% or more in bivariate processes). The study highlights the importance of examining each individual's within-person variability and offers valuable guidance for applied researchers on handling non-varying individuals.