Psychological theories often produce hypotheses that pertain to individual differences in within‐person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between‐person differences in the mean level of a certain variable and the residual within‐person variance. Currently, these approaches can be applied only when the data stem from a single variable. However, it is common practice in psychology to assess not just a single measure but rather several measures of a construct. In this paper we describe a model in which we combine the single‐indicator model with confirmatory factor analysis. The new model allows individual differences in latent mean‐level factors and latent within‐person variability factors to be estimated. Furthermore, we show how the model’s parameters can be estimated with a maximum likelihood estimator, and we illustrate the approach using an example that involves intensive longitudinal data.