Estimating the Reliability and Stability of Emotional Variability Across Time

Interest in emotional variability as an interindividual difference is growing. Yet, basic features of the construct, such as whether it can be measured reliably and whether interindividual differences remain stable over time, have not yet been investigated extensively. To improve our understanding of the construct, we examined two longitudinal data sets, each comprising two waves of daily assessments, one with a 3-month and the other with a 16-month retest interval. Overcoming a key methodological limitation of past approaches, we used Bayesian censored location scale models as an alternative modeling approach that accounts for biases introduced by bounded rating scales. The results showed that the variability estimates from the models had reliabilities around rel = .64, which can be sufficient for group-level predictions. Additionally, the latent stability of r = .60 provides evidence for stable individual differences in emotional variability, suggesting that it is more than just a transient state. Our findings partially confirm previous studies, but also suggest that the reliability and stability of intraindividual emotional variability may have been underestimated because of biases due to censored distributions, something that has often been neglected in past work. We ran exploratory analyses to further examine the influence of external events and individual life transitions on emotional variability and found that emotional variability was potentially responsive to environmental changes due to the transition from studying to working life in one data set, and due to the COVID-19 pandemic in the second data set.