Intensive longitudinal data are increasingly encountered in many research areas. For example, ecological momentary assessment (EMA) and/or mobile health (mHealth) methods are often used to study subjective experiences within changing environmental contexts. In these studies, up to 30 or 40 observations are usually obtained for each subject over a period of a week or so, allowing one to characterize a subject’s mean and variance and specify models for both. In this presentation, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances and also extend the statistical model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. The random effects are then shared in a modeling of future smoking levels. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.