This study is the first to investigate subject-level variability in sociolinguistic evaluative judgements by 30 adult L2 German learners and explore whether the observed variability is characterizable as a function of individual differences in proficiency, exposure, and motivation. Because group-level estimates did not paint an accurate picture of the individual, we propose methods capable of integrating population-level estimates with person- and ensemble-centered approaches so as to reconcile generalizability and individuality. Using random effects from Bayesian mixed-effects models, we found that global subject-level variability in evaluative judgements was not predicted by individual differences. By building homogeneous ensembles (i.e., subgroups of individuals with similar evaluative judgements), however, it was possible to assess whether ensembles were characteristic of certain levels of individual differences. This ensemble-centered approach presents an innovative way to address the "group-to-individual generalizability" issue in cross-sectional data and transcend individual variability in order to make tentative generalizations of individual cases to wider populations.